{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"from splink.duckdb.duckdb_linker import DuckDBLinker"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>uncorrupted_record</th>\n",
" <th>cluster</th>\n",
" <th>full_name</th>\n",
" <th>dob</th>\n",
" <th>birth_place</th>\n",
" <th>postcode_fake</th>\n",
" <th>lat</th>\n",
" <th>lng</th>\n",
" <th>gender</th>\n",
" <th>occupation</th>\n",
" <th>unique_id</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>True</td>\n",
" <td>Q2296770</td>\n",
" <td>thomas clifford, 1st baron clifford of chudleigh</td>\n",
" <td>1630-08-01</td>\n",
" <td>Devon</td>\n",
" <td>TQ13 8DF</td>\n",
" <td>50.692449</td>\n",
" <td>-3.813964</td>\n",
" <td>male</td>\n",
" <td>politician</td>\n",
" <td>Q2296770-1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>False</td>\n",
" <td>Q2296770</td>\n",
" <td>thomas of chudleigh</td>\n",
" <td>1630-08-01</td>\n",
" <td>Devon</td>\n",
" <td>TQ13 8DF</td>\n",
" <td>50.692449</td>\n",
" <td>-3.813964</td>\n",
" <td>male</td>\n",
" <td>politician</td>\n",
" <td>Q2296770-2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>False</td>\n",
" <td>Q2296770</td>\n",
" <td>tom 1st baron clifford of chudleigh</td>\n",
" <td>1630-08-01</td>\n",
" <td>Devon</td>\n",
" <td>TQ13 8DF</td>\n",
" <td>50.692449</td>\n",
" <td>-3.813964</td>\n",
" <td>male</td>\n",
" <td>politician</td>\n",
" <td>Q2296770-3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>False</td>\n",
" <td>Q2296770</td>\n",
" <td>thomas 1st chudleigh</td>\n",
" <td>1630-08-01</td>\n",
" <td>Devon</td>\n",
" <td>TQ13 8HU</td>\n",
" <td>50.687638</td>\n",
" <td>-3.895877</td>\n",
" <td>None</td>\n",
" <td>politician</td>\n",
" <td>Q2296770-4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>False</td>\n",
" <td>Q2296770</td>\n",
" <td>thomas clifford, 1st baron chudleigh</td>\n",
" <td>1630-08-01</td>\n",
" <td>Devon</td>\n",
" <td>TQ13 8DF</td>\n",
" <td>50.692449</td>\n",
" <td>-3.813964</td>\n",
" <td>None</td>\n",
" <td>politician</td>\n",
" <td>Q2296770-5</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" uncorrupted_record cluster \\\n",
"0 True Q2296770 \n",
"1 False Q2296770 \n",
"2 False Q2296770 \n",
"3 False Q2296770 \n",
"4 False Q2296770 \n",
"\n",
" full_name dob birth_place \\\n",
"0 thomas clifford, 1st baron clifford of chudleigh 1630-08-01 Devon \n",
"1 thomas of chudleigh 1630-08-01 Devon \n",
"2 tom 1st baron clifford of chudleigh 1630-08-01 Devon \n",
"3 thomas 1st chudleigh 1630-08-01 Devon \n",
"4 thomas clifford, 1st baron chudleigh 1630-08-01 Devon \n",
"\n",
" postcode_fake lat lng gender occupation unique_id \n",
"0 TQ13 8DF 50.692449 -3.813964 male politician Q2296770-1 \n",
"1 TQ13 8DF 50.692449 -3.813964 male politician Q2296770-2 \n",
"2 TQ13 8DF 50.692449 -3.813964 male politician Q2296770-3 \n",
"3 TQ13 8HU 50.687638 -3.895877 None politician Q2296770-4 \n",
"4 TQ13 8DF 50.692449 -3.813964 None politician Q2296770-5 "
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import pandas as pd \n",
"pd.options.display.max_rows = 1000\n",
"df = pd.read_parquet(\"./data/historical_figures_with_errors_50k.parquet\")\n",
"df.head(5)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>unique_id</th>\n",
" <th>cluster</th>\n",
" <th>full_name</th>\n",
" <th>first_and_surname</th>\n",
" <th>first_name</th>\n",
" <th>surname</th>\n",
" <th>dob</th>\n",
" <th>birth_place</th>\n",
" <th>postcode_fake</th>\n",
" <th>gender</th>\n",
" <th>occupation</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>Q2296770-1</td>\n",
" <td>Q2296770</td>\n",
" <td>thomas clifford, 1st baron clifford of chudleigh</td>\n",
" <td>thomas chudleigh</td>\n",
" <td>thomas</td>\n",
" <td>chudleigh</td>\n",
" <td>1630-08-01</td>\n",
" <td>devon</td>\n",
" <td>tq13 8df</td>\n",
" <td>male</td>\n",
" <td>politician</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>Q2296770-2</td>\n",
" <td>Q2296770</td>\n",
" <td>thomas of chudleigh</td>\n",
" <td>thomas chudleigh</td>\n",
" <td>thomas</td>\n",
" <td>chudleigh</td>\n",
" <td>1630-08-01</td>\n",
" <td>devon</td>\n",
" <td>tq13 8df</td>\n",
" <td>male</td>\n",
" <td>politician</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" unique_id cluster full_name \\\n",
"0 Q2296770-1 Q2296770 thomas clifford, 1st baron clifford of chudleigh \n",
"1 Q2296770-2 Q2296770 thomas of chudleigh \n",
"\n",
" first_and_surname first_name surname dob birth_place \\\n",
"0 thomas chudleigh thomas chudleigh 1630-08-01 devon \n",
"1 thomas chudleigh thomas chudleigh 1630-08-01 devon \n",
"\n",
" postcode_fake gender occupation \n",
"0 tq13 8df male politician \n",
"1 tq13 8df male politician "
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import numpy as np\n",
"import pandas as pd\n",
"\n",
"def clean_df(df):\n",
" cols = [\n",
" \"unique_id\",\n",
" \"cluster\",\n",
" \"full_name\",\n",
" \"dob\",\n",
" \"birth_place\",\n",
" \"postcode_fake\",\n",
" \"gender\",\n",
" \"occupation\",\n",
" ]\n",
"\n",
" df = df[cols].copy()\n",
"\n",
" df[\"name_split\"] = df[\"full_name\"].str.strip().str.split(\" \")\n",
" df[\"name_split_length\"] = df[\"name_split\"].str.len()\n",
" df[\"first_name\"] = df[\"name_split\"].str[0]\n",
" df[\"surname\"] = df[\"name_split\"].str[-1]\n",
" df[\"surname\"] = np.where(df[\"name_split_length\"] > 1, df[\"surname\"], \"\")\n",
" # df[\"middle_names\"] = df[\"name_split\"].str[1:-1]\n",
"\n",
" df[\"first_and_surname\"] = df[\"first_name\"] + \" \" + df[\"surname\"]\n",
"\n",
" for col in [\n",
" \"full_name\",\n",
" \"first_and_surname\",\n",
" \"first_name\",\n",
" \"surname\",\n",
" \"dob\",\n",
" \"birth_place\",\n",
" \"postcode_fake\",\n",
" \"gender\",\n",
" \"occupation\",\n",
" ]:\n",
" df[col] = df[col].str.lower().str.strip()\n",
" df[col] = df[col].replace({\"\": None})\n",
"\n",
" cols = [\n",
" \"unique_id\",\n",
" \"cluster\",\n",
" \"full_name\",\n",
" \"first_and_surname\",\n",
" \"first_name\",\n",
" \"surname\",\n",
" \"dob\",\n",
" \"birth_place\",\n",
" \"postcode_fake\",\n",
" \"gender\",\n",
" \"occupation\",\n",
" ]\n",
" return df[cols]\n",
"\n",
"\n",
"df_clean = clean_df(df)\n",
"df_clean.head(2)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
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"config": {
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},
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",
"text/plain": [
"<VegaLite 4 object>\n",
"\n",
"If you see this message, it means the renderer has not been properly enabled\n",
"for the frontend that you are using. For more information, see\n",
"https://altair-viz.github.io/user_guide/troubleshooting.html\n"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Initialise the linker, passing in the input dataset(s)\n",
"linker = DuckDBLinker(df_clean, connection=\":temporary:\")\n",
"\n",
"import altair as alt\n",
"alt.renderers.enable('mimetype')\n",
"linker.profile_columns([\"first_name\", \"postcode_fake\", \"substr(dob, 1,4)\"], top_n=10, bottom_n=5)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'count_of_pairwise_comparisons_generated': 16372982}"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"linker.compute_number_of_comparisons_generated_by_blocking_rule(\"l.first_name = r.first_name\")"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'count_of_pairwise_comparisons_generated': 243656}"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"linker.compute_number_of_comparisons_generated_by_blocking_rule(\"l.first_name = r.first_name and l.surname = r.surname\",)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"import splink.duckdb.duckdb_comparison_library as cl\n",
"\n",
"settings = {\n",
" \"probability_two_random_records_match\": 9/50_000,\n",
" \"link_type\": \"dedupe_only\",\n",
" \"blocking_rules_to_generate_predictions\": [\n",
" \"l.first_name = r.first_name and l.surname = r.surname\",\n",
" \"l.surname = r.surname and l.dob = r.dob\",\n",
" \"l.first_name = r.first_name and l.dob = r.dob\",\n",
" \"l.postcode_fake = r.postcode_fake and l.first_name = r.first_name\",\n",
" ],\n",
" \"comparisons\": [\n",
" cl.jaccard_at_thresholds(\"first_name\", [0.9, 0.5], term_frequency_adjustments=False),\n",
" cl.jaccard_at_thresholds(\"surname\", [0.9, 0.5], term_frequency_adjustments=False),\n",
" cl.levenshtein_at_thresholds(\"dob\", [1,2], term_frequency_adjustments=False),\n",
" cl.levenshtein_at_thresholds(\"postcode_fake\", 2),\n",
" cl.exact_match(\"birth_place\", term_frequency_adjustments=False),\n",
" cl.exact_match(\"occupation\", term_frequency_adjustments=False),\n",
" ],\n",
" \"retain_matching_columns\": True,\n",
" \"retain_intermediate_calculation_columns\": True,\n",
" \"max_iterations\": 10,\n",
" \"em_convergence\": 0.01\n",
"}"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"linker.initialise_settings(settings)"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"----- Estimating u probabilities using random sampling -----\n",
"\n",
"Estimated u probabilities using random sampling\n",
"\n",
"Your model is not yet fully trained. Missing estimates for:\n",
" - first_name (no m values are trained).\n",
" - surname (no m values are trained).\n",
" - dob (no m values are trained).\n",
" - postcode_fake (no m values are trained).\n",
" - birth_place (no m values are trained).\n",
" - occupation (no m values are trained).\n"
]
}
],
"source": [
"linker.estimate_u_using_random_sampling(target_rows=5e6)"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"\n",
"----- Starting EM training session -----\n",
"\n",
"Estimating the m probabilities of the model by blocking on:\n",
"l.first_name = r.first_name and l.surname = r.surname\n",
"\n",
"Parameter estimates will be made for the following comparison(s):\n",
" - dob\n",
" - postcode_fake\n",
" - birth_place\n",
" - occupation\n",
"\n",
"Parameter estimates cannot be made for the following comparison(s) since they are used in the blocking rules: \n",
" - first_name\n",
" - surname\n",
"\n",
"Iteration 1: Largest change in params was -0.527 in probability_two_random_records_match\n",
"Iteration 2: Largest change in params was -0.0345 in probability_two_random_records_match\n",
"Iteration 3: Largest change in params was -0.0147 in the m_probability of birth_place, level `All other comparisons`\n",
"Iteration 4: Largest change in params was -0.00748 in the m_probability of dob, level `All other comparisons`\n",
"\n",
"EM converged after 4 iterations\n",
"\n",
"Your model is not yet fully trained. Missing estimates for:\n",
" - first_name (no m values are trained).\n",
" - surname (no m values are trained).\n"
]
},
{
"data": {
"application/vnd.vegalite.v4+json": {
"$schema": "https://vega.github.io/schema/vega-lite/v5.2.json",
"config": {
"header": {
"title": null
},
"mark": {
"tooltip": null
},
"title": {
"anchor": "middle"
},
"view": {
"height": 60,
"width": 400
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"data": {
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},
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"u_probability_description": "Amongst non-matching record comparisons, 7.96% of records are in the levenshtein <= 2 comparison level"
},
{
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"bayes_factor_description": "If comparison level is `all other comparisons` then comparison is 53.70 times less likely to be a match",
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"iteration": 0,
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"m_probability_description": "Amongst matching record comparisons, 95.00% of records are in the exact match comparison level",
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"tf_adjustment_weight": 1,
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"u_probability_description": "Amongst non-matching record comparisons, 0.02% of records are in the exact match comparison level"
},
{
"bayes_factor": 40.74109828141787,
"bayes_factor_description": "If comparison level is `levenshtein <= 2` then comparison is 40.74 times more likely to be a match",
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"iteration": 0,
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"m_probability_description": "Amongst matching record comparisons, 2.50% of records are in the levenshtein <= 2 comparison level",
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"tf_adjustment_weight": 1,
"u_probability": 0.0006136309784118558,
"u_probability_description": "Amongst non-matching record comparisons, 0.06% of records are in the levenshtein <= 2 comparison level"
},
{
"bayes_factor": 0.025019219568439496,
"bayes_factor_description": "If comparison level is `all other comparisons` then comparison is 39.97 times less likely to be a match",
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},
{
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"bayes_factor_description": "If comparison level is `exact match` then comparison is 176.24 times more likely to be a match",
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},
{
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"comparison_name": "birth_place",
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"is_null_level": false,
"iteration": 0,
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"m_probability": 0.050000000000000044,
"m_probability_description": "Amongst matching record comparisons, 5.00% of records are in the all other comparisons comparison level",
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},
{
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"bayes_factor_description": "If comparison level is `exact match` then comparison is 27.29 times more likely to be a match",
"comparison_name": "occupation",
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"iteration": 0,
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},
{
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"bayes_factor_description": "If comparison level is `all other comparisons` then comparison is 19.30 times less likely to be a match",
"comparison_name": "occupation",
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},
{
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"sql_condition": "levenshtein(dob_l, dob_r) <= 1",
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"u_probability": 0.023123510977211607,
"u_probability_description": "Amongst non-matching record comparisons, 2.31% of records are in the levenshtein <= 1 comparison level"
},
{
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"bayes_factor_description": "If comparison level is `levenshtein <= 2` then comparison is 1.95 times less likely to be a match",
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"is_null_level": false,
"iteration": 1,
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"m_probability_description": "Amongst matching record comparisons, 4.08% of records are in the levenshtein <= 2 comparison level",
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"sql_condition": "levenshtein(dob_l, dob_r) <= 2",
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"u_probability": 0.07958216344133114,
"u_probability_description": "Amongst non-matching record comparisons, 7.96% of records are in the levenshtein <= 2 comparison level"
},
{
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"bayes_factor_description": "If comparison level is `all other comparisons` then comparison is 18.29 times less likely to be a match",
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"is_null_level": false,
"iteration": 1,
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"u_probability": 0.8949940880033891,
"u_probability_description": "Amongst non-matching record comparisons, 89.50% of records are in the all other comparisons comparison level"
},
{
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"iteration": 1,
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"u_probability_description": "Amongst non-matching record comparisons, 0.02% of records are in the exact match comparison level"
},
{
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"m_probability_description": "Amongst matching record comparisons, 13.84% of records are in the levenshtein <= 2 comparison level",
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"sql_condition": "levenshtein(postcode_fake_l, postcode_fake_r) <= 2",
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"u_probability": 0.0006136309784118558,
"u_probability_description": "Amongst non-matching record comparisons, 0.06% of records are in the levenshtein <= 2 comparison level"
},
{
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"bayes_factor_description": "If comparison level is `all other comparisons` then comparison is 5.31 times less likely to be a match",
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"iteration": 1,
"label_for_charts": "All other comparisons",
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"m_probability_description": "Amongst matching record comparisons, 18.81% of records are in the all other comparisons comparison level",
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"tf_adjustment_weight": 1,
"u_probability": 0.9992318078352964,
"u_probability_description": "Amongst non-matching record comparisons, 99.92% of records are in the all other comparisons comparison level"
},
{
"bayes_factor": 144.06962885102888,
"bayes_factor_description": "If comparison level is `exact match` then comparison is 144.07 times more likely to be a match",
"comparison_name": "birth_place",
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"comparison_vector_value": 1,
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"iteration": 1,
"label_for_charts": "Exact match",
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"m_probability": 0.7765872026366752,
"m_probability_description": "Amongst matching record comparisons, 77.66% of records are in the exact match comparison level",
"max_comparison_vector_value": 1,
"probability_two_random_records_match": 0.36731601956729415,
"sql_condition": "birth_place_l = birth_place_r",
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"tf_adjustment_weight": 1,
"u_probability": 0.005390360264200329,
"u_probability_description": "Amongst non-matching record comparisons, 0.54% of records are in the exact match comparison level"
},
{
"bayes_factor": 0.22462359948858213,
"bayes_factor_description": "If comparison level is `all other comparisons` then comparison is 4.45 times less likely to be a match",
"comparison_name": "birth_place",
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"comparison_vector_value": 0,
"has_tf_adjustments": false,
"is_null_level": false,
"iteration": 1,
"label_for_charts": "All other comparisons",
"log2_bayes_factor": -2.1544185862202636,
"m_probability": 0.22341279736349723,
"m_probability_description": "Amongst matching record comparisons, 22.34% of records are in the all other comparisons comparison level",
"max_comparison_vector_value": 1,
"probability_two_random_records_match": 0.36731601956729415,
"sql_condition": "ELSE",
"tf_adjustment_column": null,
"tf_adjustment_weight": 1,
"u_probability": 0.9946096397357996,
"u_probability_description": "Amongst non-matching record comparisons, 99.46% of records are in the all other comparisons comparison level"
},
{
"bayes_factor": 25.810054400191795,
"bayes_factor_description": "If comparison level is `exact match` then comparison is 25.81 times more likely to be a match",
"comparison_name": "occupation",
"comparison_sort_order": 3,
"comparison_vector_value": 1,
"has_tf_adjustments": false,
"is_null_level": false,
"iteration": 1,
"label_for_charts": "Exact match",
"log2_bayes_factor": 4.6898612771098,
"m_probability": 0.8984034703011985,
"m_probability_description": "Amongst matching record comparisons, 89.84% of records are in the exact match comparison level",
"max_comparison_vector_value": 1,
"probability_two_random_records_match": 0.36731601956729415,
"sql_condition": "occupation_l = occupation_r",
"tf_adjustment_column": null,
"tf_adjustment_weight": 1,
"u_probability": 0.03480827496026209,
"u_probability_description": "Amongst non-matching record comparisons, 3.48% of records are in the exact match comparison level"
},
{
"bayes_factor": 0.10526046490393261,
"bayes_factor_description": "If comparison level is `all other comparisons` then comparison is 9.50 times less likely to be a match",
"comparison_name": "occupation",
"comparison_sort_order": 3,
"comparison_vector_value": 0,
"has_tf_adjustments": false,
"is_null_level": false,
"iteration": 1,
"label_for_charts": "All other comparisons",
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"m_probability": 0.10159652969911151,
"m_probability_description": "Amongst matching record comparisons, 10.16% of records are in the all other comparisons comparison level",
"max_comparison_vector_value": 1,
"probability_two_random_records_match": 0.36731601956729415,
"sql_condition": "ELSE",
"tf_adjustment_column": null,
"tf_adjustment_weight": 1,
"u_probability": 0.9651917250397379,
"u_probability_description": "Amongst non-matching record comparisons, 96.52% of records are in the all other comparisons comparison level"
},
{
"bayes_factor": 0.6103957500270338,
"bayes_factor_description": "The probability that two random records drawn at random match is 0.379 or one in 2.6 records.This is equivalent to a starting match weight of -0.712.",
"comparison_name": "probability_two_random_records_match",
"comparison_sort_order": -1,
"comparison_vector_value": 0,
"has_tf_adjustments": false,
"is_null_level": false,
"iteration": 2,
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"m_probability": null,
"m_probability_description": null,
"max_comparison_vector_value": 0,
"probability_two_random_records_match": 0.36731601956729415,
"sql_condition": null,
"tf_adjustment_column": null,
"tf_adjustment_weight": null,
"u_probability": null,
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},
{
"bayes_factor": 259.14246234679655,
"bayes_factor_description": "If comparison level is `exact match` then comparison is 259.14 times more likely to be a match",
"comparison_name": "dob",
"comparison_sort_order": 0,
"comparison_vector_value": 3,
"has_tf_adjustments": false,
"is_null_level": false,
"iteration": 2,
"label_for_charts": "Exact match",
"log2_bayes_factor": 8.017601620568344,
"m_probability": 0.5960892299632095,
"m_probability_description": "Amongst matching record comparisons, 59.61% of records are in the exact match comparison level",
"max_comparison_vector_value": 3,
"probability_two_random_records_match": 0.36731601956729415,
"sql_condition": "dob_l = dob_r",
"tf_adjustment_column": null,
"tf_adjustment_weight": 1,
"u_probability": 0.00230023757806814,
"u_probability_description": "Amongst non-matching record comparisons, 0.23% of records are in the exact match comparison level"
},
{
"bayes_factor": 14.309529621181515,
"bayes_factor_description": "If comparison level is `levenshtein <= 1` then comparison is 14.31 times more likely to be a match",
"comparison_name": "dob",
"comparison_sort_order": 0,
"comparison_vector_value": 2,
"has_tf_adjustments": false,
"is_null_level": false,
"iteration": 2,
"label_for_charts": "levenshtein <= 1",
"log2_bayes_factor": 3.8389043438672306,
"m_probability": 0.3308865652741254,
"m_probability_description": "Amongst matching record comparisons, 33.09% of records are in the levenshtein <= 1 comparison level",
"max_comparison_vector_value": 3,
"probability_two_random_records_match": 0.36731601956729415,
"sql_condition": "levenshtein(dob_l, dob_r) <= 1",
"tf_adjustment_column": null,
"tf_adjustment_weight": 1,
"u_probability": 0.023123510977211607,
"u_probability_description": "Amongst non-matching record comparisons, 2.31% of records are in the levenshtein <= 1 comparison level"
},
{
"bayes_factor": 0.48568710774004464,
"bayes_factor_description": "If comparison level is `levenshtein <= 2` then comparison is 2.06 times less likely to be a match",
"comparison_name": "dob",
"comparison_sort_order": 0,
"comparison_vector_value": 1,
"has_tf_adjustments": false,
"is_null_level": false,
"iteration": 2,
"label_for_charts": "levenshtein <= 2",
"log2_bayes_factor": -1.0419009034533753,
"m_probability": 0.03865203078951564,
"m_probability_description": "Amongst matching record comparisons, 3.87% of records are in the levenshtein <= 2 comparison level",
"max_comparison_vector_value": 3,
"probability_two_random_records_match": 0.36731601956729415,
"sql_condition": "levenshtein(dob_l, dob_r) <= 2",
"tf_adjustment_column": null,
"tf_adjustment_weight": 1,
"u_probability": 0.07958216344133114,
"u_probability_description": "Amongst non-matching record comparisons, 7.96% of records are in the levenshtein <= 2 comparison level"
},
{
"bayes_factor": 0.03840491734448192,
"bayes_factor_description": "If comparison level is `all other comparisons` then comparison is 26.04 times less likely to be a match",
"comparison_name": "dob",
"comparison_sort_order": 0,
"comparison_vector_value": 0,
"has_tf_adjustments": false,
"is_null_level": false,
"iteration": 2,
"label_for_charts": "All other comparisons",
"log2_bayes_factor": -4.702565145122343,
"m_probability": 0.03437217397357013,
"m_probability_description": "Amongst matching record comparisons, 3.44% of records are in the all other comparisons comparison level",
"max_comparison_vector_value": 3,
"probability_two_random_records_match": 0.36731601956729415,
"sql_condition": "ELSE",
"tf_adjustment_column": null,
"tf_adjustment_weight": 1,
"u_probability": 0.8949940880033891,
"u_probability_description": "Amongst non-matching record comparisons, 89.50% of records are in the all other comparisons comparison level"
},
{
"bayes_factor": 4316.411377384189,
"bayes_factor_description": "If comparison level is `exact match` then comparison is 4,316.41 times more likely to be a match",
"comparison_name": "postcode_fake",
"comparison_sort_order": 1,
"comparison_vector_value": 2,
"has_tf_adjustments": false,
"is_null_level": false,
"iteration": 2,
"label_for_charts": "Exact match",
"log2_bayes_factor": 12.075616652692302,
"m_probability": 0.6671496630115258,
"m_probability_description": "Amongst matching record comparisons, 66.71% of records are in the exact match comparison level",
"max_comparison_vector_value": 2,
"probability_two_random_records_match": 0.36731601956729415,
"sql_condition": "postcode_fake_l = postcode_fake_r",
"tf_adjustment_column": null,
"tf_adjustment_weight": 1,
"u_probability": 0.00015456118629170805,
"u_probability_description": "Amongst non-matching record comparisons, 0.02% of records are in the exact match comparison level"
},
{
"bayes_factor": 224.33667729624497,
"bayes_factor_description": "If comparison level is `levenshtein <= 2` then comparison is 224.34 times more likely to be a match",
"comparison_name": "postcode_fake",
"comparison_sort_order": 1,
"comparison_vector_value": 1,
"has_tf_adjustments": false,
"is_null_level": false,
"iteration": 2,
"label_for_charts": "levenshtein <= 2",
"log2_bayes_factor": 7.809521698864289,
"m_probability": 0.13765993478295954,
"m_probability_description": "Amongst matching record comparisons, 13.77% of records are in the levenshtein <= 2 comparison level",
"max_comparison_vector_value": 2,
"probability_two_random_records_match": 0.36731601956729415,
"sql_condition": "levenshtein(postcode_fake_l, postcode_fake_r) <= 2",
"tf_adjustment_column": null,
"tf_adjustment_weight": 1,
"u_probability": 0.0006136309784118558,
"u_probability_description": "Amongst non-matching record comparisons, 0.06% of records are in the levenshtein <= 2 comparison level"
},
{
"bayes_factor": 0.19534046121766735,
"bayes_factor_description": "If comparison level is `all other comparisons` then comparison is 5.12 times less likely to be a match",
"comparison_name": "postcode_fake",
"comparison_sort_order": 1,
"comparison_vector_value": 0,
"has_tf_adjustments": false,
"is_null_level": false,
"iteration": 2,
"label_for_charts": "All other comparisons",
"log2_bayes_factor": -2.355937286716486,
"m_probability": 0.19519040220591036,
"m_probability_description": "Amongst matching record comparisons, 19.52% of records are in the all other comparisons comparison level",
"max_comparison_vector_value": 2,
"probability_two_random_records_match": 0.36731601956729415,
"sql_condition": "ELSE",
"tf_adjustment_column": null,
"tf_adjustment_weight": 1,
"u_probability": 0.9992318078352964,
"u_probability_description": "Amongst non-matching record comparisons, 99.92% of records are in the all other comparisons comparison level"
},
{
"bayes_factor": 149.8252267246725,
"bayes_factor_description": "If comparison level is `exact match` then comparison is 149.83 times more likely to be a match",
"comparison_name": "birth_place",
"comparison_sort_order": 2,
"comparison_vector_value": 1,
"has_tf_adjustments": false,
"is_null_level": false,
"iteration": 2,
"label_for_charts": "Exact match",
"log2_bayes_factor": 7.227136746858861,
"m_probability": 0.8076119487114797,
"m_probability_description": "Amongst matching record comparisons, 80.76% of records are in the exact match comparison level",
"max_comparison_vector_value": 1,
"probability_two_random_records_match": 0.36731601956729415,
"sql_condition": "birth_place_l = birth_place_r",
"tf_adjustment_column": null,
"tf_adjustment_weight": 1,
"u_probability": 0.005390360264200329,
"u_probability_description": "Amongst non-matching record comparisons, 0.54% of records are in the exact match comparison level"
},
{
"bayes_factor": 0.19343071251560998,
"bayes_factor_description": "If comparison level is `all other comparisons` then comparison is 5.17 times less likely to be a match",
"comparison_name": "birth_place",
"comparison_sort_order": 2,
"comparison_vector_value": 0,
"has_tf_adjustments": false,
"is_null_level": false,
"iteration": 2,
"label_for_charts": "All other comparisons",
"log2_bayes_factor": -2.370111213846395,
"m_probability": 0.19238805128898986,
"m_probability_description": "Amongst matching record comparisons, 19.24% of records are in the all other comparisons comparison level",
"max_comparison_vector_value": 1,
"probability_two_random_records_match": 0.36731601956729415,
"sql_condition": "ELSE",
"tf_adjustment_column": null,
"tf_adjustment_weight": 1,
"u_probability": 0.9946096397357996,
"u_probability_description": "Amongst non-matching record comparisons, 99.46% of records are in the all other comparisons comparison level"
},
{
"bayes_factor": 26.080195723205453,
"bayes_factor_description": "If comparison level is `exact match` then comparison is 26.08 times more likely to be a match",
"comparison_name": "occupation",
"comparison_sort_order": 3,
"comparison_vector_value": 1,
"has_tf_adjustments": false,
"is_null_level": false,
"iteration": 2,
"label_for_charts": "Exact match",
"log2_bayes_factor": 4.704882791444019,
"m_probability": 0.9078066237507869,
"m_probability_description": "Amongst matching record comparisons, 90.78% of records are in the exact match comparison level",
"max_comparison_vector_value": 1,
"probability_two_random_records_match": 0.36731601956729415,
"sql_condition": "occupation_l = occupation_r",
"tf_adjustment_column": null,
"tf_adjustment_weight": 1,
"u_probability": 0.03480827496026209,
"u_probability_description": "Amongst non-matching record comparisons, 3.48% of records are in the exact match comparison level"
},
{
"bayes_factor": 0.09551820001953051,
"bayes_factor_description": "If comparison level is `all other comparisons` then comparison is 10.47 times less likely to be a match",
"comparison_name": "occupation",
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"comparison_vector_value": 0,
"has_tf_adjustments": false,
"is_null_level": false,
"iteration": 2,
"label_for_charts": "All other comparisons",
"log2_bayes_factor": -3.3880805395975155,
"m_probability": 0.09219337624954138,
"m_probability_description": "Amongst matching record comparisons, 9.22% of records are in the all other comparisons comparison level",
"max_comparison_vector_value": 1,
"probability_two_random_records_match": 0.36731601956729415,
"sql_condition": "ELSE",
"tf_adjustment_column": null,
"tf_adjustment_weight": 1,
"u_probability": 0.9651917250397379,
"u_probability_description": "Amongst non-matching record comparisons, 96.52% of records are in the all other comparisons comparison level"
},
{
"bayes_factor": 0.5901235425012258,
"bayes_factor_description": "The probability that two random records drawn at random match is 0.371 or one in 2.7 records.This is equivalent to a starting match weight of -0.761.",
"comparison_name": "probability_two_random_records_match",
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"iteration": 3,
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},
{
"bayes_factor": 263.0115618703464,
"bayes_factor_description": "If comparison level is `exact match` then comparison is 263.01 times more likely to be a match",
"comparison_name": "dob",
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"comparison_vector_value": 3,
"has_tf_adjustments": false,
"is_null_level": false,
"iteration": 3,
"label_for_charts": "Exact match",
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"m_probability": 0.6049890780805642,
"m_probability_description": "Amongst matching record comparisons, 60.50% of records are in the exact match comparison level",
"max_comparison_vector_value": 3,
"probability_two_random_records_match": 0.36731601956729415,
"sql_condition": "dob_l = dob_r",
"tf_adjustment_column": null,
"tf_adjustment_weight": 1,
"u_probability": 0.00230023757806814,
"u_probability_description": "Amongst non-matching record comparisons, 0.23% of records are in the exact match comparison level"
},
{
"bayes_factor": 14.469817973916678,
"bayes_factor_description": "If comparison level is `levenshtein <= 1` then comparison is 14.47 times more likely to be a match",
"comparison_name": "dob",
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"comparison_vector_value": 2,
"has_tf_adjustments": false,
"is_null_level": false,
"iteration": 3,
"label_for_charts": "levenshtein <= 1",
"log2_bayes_factor": 3.8549748681889637,
"m_probability": 0.3345929947581161,
"m_probability_description": "Amongst matching record comparisons, 33.46% of records are in the levenshtein <= 1 comparison level",
"max_comparison_vector_value": 3,
"probability_two_random_records_match": 0.36731601956729415,
"sql_condition": "levenshtein(dob_l, dob_r) <= 1",
"tf_adjustment_column": null,
"tf_adjustment_weight": 1,
"u_probability": 0.023123510977211607,
"u_probability_description": "Amongst non-matching record comparisons, 2.31% of records are in the levenshtein <= 1 comparison level"
},
{
"bayes_factor": 0.47761167275868416,
"bayes_factor_description": "If comparison level is `levenshtein <= 2` then comparison is 2.09 times less likely to be a match",
"comparison_name": "dob",
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"comparison_vector_value": 1,
"has_tf_adjustments": false,
"is_null_level": false,
"iteration": 3,
"label_for_charts": "levenshtein <= 2",
"log2_bayes_factor": -1.0660899986002241,
"m_probability": 0.03800937020296916,
"m_probability_description": "Amongst matching record comparisons, 3.80% of records are in the levenshtein <= 2 comparison level",
"max_comparison_vector_value": 3,
"probability_two_random_records_match": 0.36731601956729415,
"sql_condition": "levenshtein(dob_l, dob_r) <= 2",
"tf_adjustment_column": null,
"tf_adjustment_weight": 1,
"u_probability": 0.07958216344133114,
"u_probability_description": "Amongst non-matching record comparisons, 7.96% of records are in the levenshtein <= 2 comparison level"
},
{
"bayes_factor": 0.025037659196964417,
"bayes_factor_description": "If comparison level is `all other comparisons` then comparison is 39.94 times less likely to be a match",
"comparison_name": "dob",
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"has_tf_adjustments": false,
"is_null_level": false,
"iteration": 3,
"label_for_charts": "All other comparisons",
"log2_bayes_factor": -5.319756500618782,
"m_probability": 0.022408556958726837,
"m_probability_description": "Amongst matching record comparisons, 2.24% of records are in the all other comparisons comparison level",
"max_comparison_vector_value": 3,
"probability_two_random_records_match": 0.36731601956729415,
"sql_condition": "ELSE",
"tf_adjustment_column": null,
"tf_adjustment_weight": 1,
"u_probability": 0.8949940880033891,
"u_probability_description": "Amongst non-matching record comparisons, 89.50% of records are in the all other comparisons comparison level"
},
{
"bayes_factor": 4363.6065049448525,
"bayes_factor_description": "If comparison level is `exact match` then comparison is 4,363.61 times more likely to be a match",
"comparison_name": "postcode_fake",
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"comparison_vector_value": 2,
"has_tf_adjustments": false,
"is_null_level": false,
"iteration": 3,
"label_for_charts": "Exact match",
"log2_bayes_factor": 12.09130529490284,
"m_probability": 0.6744441979144904,
"m_probability_description": "Amongst matching record comparisons, 67.44% of records are in the exact match comparison level",
"max_comparison_vector_value": 2,
"probability_two_random_records_match": 0.36731601956729415,
"sql_condition": "postcode_fake_l = postcode_fake_r",
"tf_adjustment_column": null,
"tf_adjustment_weight": 1,
"u_probability": 0.00015456118629170805,
"u_probability_description": "Amongst non-matching record comparisons, 0.02% of records are in the exact match comparison level"
},
{
"bayes_factor": 226.47303675947256,
"bayes_factor_description": "If comparison level is `levenshtein <= 2` then comparison is 226.47 times more likely to be a match",
"comparison_name": "postcode_fake",
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"has_tf_adjustments": false,
"is_null_level": false,
"iteration": 3,
"label_for_charts": "levenshtein <= 2",
"log2_bayes_factor": 7.82319548707074,
"m_probability": 0.13897087113061932,
"m_probability_description": "Amongst matching record comparisons, 13.90% of records are in the levenshtein <= 2 comparison level",
"max_comparison_vector_value": 2,
"probability_two_random_records_match": 0.36731601956729415,
"sql_condition": "levenshtein(postcode_fake_l, postcode_fake_r) <= 2",
"tf_adjustment_column": null,
"tf_adjustment_weight": 1,
"u_probability": 0.0006136309784118558,
"u_probability_description": "Amongst non-matching record comparisons, 0.06% of records are in the levenshtein <= 2 comparison level"
},
{
"bayes_factor": 0.18672837422942887,
"bayes_factor_description": "If comparison level is `all other comparisons` then comparison is 5.36 times less likely to be a match",
"comparison_name": "postcode_fake",
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"comparison_vector_value": 0,
"has_tf_adjustments": false,
"is_null_level": false,
"iteration": 3,
"label_for_charts": "All other comparisons",
"log2_bayes_factor": -2.420986926563895,
"m_probability": 0.18658493095541798,
"m_probability_description": "Amongst matching record comparisons, 18.66% of records are in the all other comparisons comparison level",
"max_comparison_vector_value": 2,
"probability_two_random_records_match": 0.36731601956729415,
"sql_condition": "ELSE",
"tf_adjustment_column": null,
"tf_adjustment_weight": 1,
"u_probability": 0.9992318078352964,
"u_probability_description": "Amongst non-matching record comparisons, 99.92% of records are in the all other comparisons comparison level"
},
{
"bayes_factor": 152.55265993455592,
"bayes_factor_description": "If comparison level is `exact match` then comparison is 152.55 times more likely to be a match",
"comparison_name": "birth_place",
"comparison_sort_order": 2,
"comparison_vector_value": 1,
"has_tf_adjustments": false,
"is_null_level": false,
"iteration": 3,
"label_for_charts": "Exact match",
"log2_bayes_factor": 7.253163524990785,
"m_probability": 0.8223137963092958,
"m_probability_description": "Amongst matching record comparisons, 82.23% of records are in the exact match comparison level",
"max_comparison_vector_value": 1,
"probability_two_random_records_match": 0.36731601956729415,
"sql_condition": "birth_place_l = birth_place_r",
"tf_adjustment_column": null,
"tf_adjustment_weight": 1,
"u_probability": 0.005390360264200329,
"u_probability_description": "Amongst non-matching record comparisons, 0.54% of records are in the exact match comparison level"
},
{
"bayes_factor": 0.17864918717081726,
"bayes_factor_description": "If comparison level is `all other comparisons` then comparison is 5.60 times less likely to be a match",
"comparison_name": "birth_place",
"comparison_sort_order": 2,
"comparison_vector_value": 0,
"has_tf_adjustments": false,
"is_null_level": false,
"iteration": 3,
"label_for_charts": "All other comparisons",
"log2_bayes_factor": -2.484798745012871,
"m_probability": 0.17768620369106,
"m_probability_description": "Amongst matching record comparisons, 17.77% of records are in the all other comparisons comparison level",
"max_comparison_vector_value": 1,
"probability_two_random_records_match": 0.36731601956729415,
"sql_condition": "ELSE",
"tf_adjustment_column": null,
"tf_adjustment_weight": 1,
"u_probability": 0.9946096397357996,
"u_probability_description": "Amongst non-matching record comparisons, 99.46% of records are in the all other comparisons comparison level"
},
{
"bayes_factor": 26.080210061831067,
"bayes_factor_description": "If comparison level is `exact match` then comparison is 26.08 times more likely to be a match",
"comparison_name": "occupation",
"comparison_sort_order": 3,
"comparison_vector_value": 1,
"has_tf_adjustments": false,
"is_null_level": false,
"iteration": 3,
"label_for_charts": "Exact match",
"log2_bayes_factor": 4.704883584622821,
"m_probability": 0.9078071228536098,
"m_probability_description": "Amongst matching record comparisons, 90.78% of records are in the exact match comparison level",
"max_comparison_vector_value": 1,
"probability_two_random_records_match": 0.36731601956729415,
"sql_condition": "occupation_l = occupation_r",
"tf_adjustment_column": null,
"tf_adjustment_weight": 1,
"u_probability": 0.03480827496026209,
"u_probability_description": "Amongst non-matching record comparisons, 3.48% of records are in the exact match comparison level"
},
{
"bayes_factor": 0.09551768291739937,
"bayes_factor_description": "If comparison level is `all other comparisons` then comparison is 10.47 times less likely to be a match",
"comparison_name": "occupation",
"comparison_sort_order": 3,
"comparison_vector_value": 0,
"has_tf_adjustments": false,
"is_null_level": false,
"iteration": 3,
"label_for_charts": "All other comparisons",
"log2_bayes_factor": -3.388088349865082,
"m_probability": 0.0921928771468434,
"m_probability_description": "Amongst matching record comparisons, 9.22% of records are in the all other comparisons comparison level",
"max_comparison_vector_value": 1,
"probability_two_random_records_match": 0.36731601956729415,
"sql_condition": "ELSE",
"tf_adjustment_column": null,
"tf_adjustment_weight": 1,
"u_probability": 0.9651917250397379,
"u_probability_description": "Amongst non-matching record comparisons, 96.52% of records are in the all other comparisons comparison level"
},
{
"bayes_factor": 0.5805679153059622,
"bayes_factor_description": "The probability that two random records drawn at random match is 0.367 or one in 2.7 records.This is equivalent to a starting match weight of -0.784.",
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{
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2H/ozzylecBY+EUrD9STii+mPQLzb7ioRQKUMQh74cCLxMUZ4i9vWDu88WgdDRrA9deZZIaaYj80goFIXbI1ufBC7+OR4AZWPt2B7+bh2YUYAMv+2ZAqkkwIGqOlkLavrHoBKXB4xmQ5aGPiBTcDI7YOKpwGAJSbPJf7OYg+8ICyuYbBxniWmZ13cJ/FlDEJM4bnpSkAXzwT/zW0fVDc4AIHOs4K3BhBjOo8pXjw6ACO/Z69LBjBgk4GKqbkgoOI14/fAGolYOiCI8ol9ixdQgQQ8wMTEObijPOeNZdAMLhzD6wfUxNLAeTCDHlTi7wAkt8CJ8vHi4BkkPjaY8JYCmwzu2AxvH/8frBveSLe/LTanbHQjMYADQXxoAGHAu9PNDdpuin/RokUKjZExqEA4wEIiTAH7AjN59R0HqA5InNeLPkRbAUH6GjDp6gqgUb+88vIRA6jQ7wAa9MEGbjEd5dEHWSznYl2drsTmArXMILgEvNAugM3FL1I+8bCRidhK+heao32sGNRY7Sb0hXxOTz4eAEbuRWhMrH1QE7VfbvehHwCJPHtuyt2FTbj20if5eOU9EAmo7oMwGKIT1Mnt54oNsJULDeJjZ82aNRpyREKH4HZaxJPT59kxxIXdEC7C9bwz+Cgm4fnmHeXCAdwiKcCW/rBp0yad/XF///333/dor3s/RQuXseHEFAi7AgaoYbeQ1S9pCrDACYBloRKDQzARnwr4ASWRf+M6pufxzjFNSkxrPKfgOEAl3pDBHq8tCzLcwEW5DCqEGRAzGE+ZgCX1YDGQCxfwEQhNGEyBlsjYUKawaTeDIHXLjwa0malyFka5ad7I+gKBtIePDIDHJcAYsEEbt8DL/c3VBdjFwx0JvfFq4mJQgSZsz9RttJN3cus7ed0Lm6EDZdOWvPa7DJZHOwERbOPsg16ur9IXg/0psi7ck/5C6EpBnyhEPfFwp/q+ud0H7fk7mgODJGZc0I+t3pxHNS8b+v6de/IhzD1jndrFDAvvAhYt8tFNCAMfzoT+MKsQTwq2l+v58GUHBgDVkimQbgoYoKabxay+aaNAEFAL6kSmtBEnoqLOs4WHDa9eQaUgoBIvaskUKCwF2L0DTz4puGVZfk9Xw9NK3DozSsQkWzIF0k0BA9R0s5jVN20UYKsqpqnZSSDaBvVp05ACqCieZOLk8BwR9lBQiSlspmeZrsW7ZckUKEwFiFnlI41QFGYG8J7md/9SPpCJZSccwJIpkI4KGKCmo9WszqaAKWAKmAKmgClgCmSwAgaoGWxca5opYAqYAqaAKWAKmALpqIABajpazeocagVY2MKinXgWPRV0Q6hbIot0klU/VtyzwKww7p2sNhRkOazqZiV+5MK1gqxDOt2rsPp1OmlkdTUF0k0BA9R0s1g+68teiGxZEi2xV6g7Szqv4tmKiC2E3L6G0a53m1izMjodFp4ku76s1GbvzsjFPm6jfrchfV5aR/6djeDZ/JuVvflNrGRmP9HIozHzW57Lxz6bbNnDJuSRie2s2ICcuDq2gmKrIxYnxZMANTakJw4vW2CNbY04cIIN2dGVPWiD2xTlpRtbbrGtEv0wGxJH2LJFmzsxLZ4+mQ26WBtNgXRXwAA13S0YZ/3ZqgYQI7GinK2W3MbNQIs73jGv4tgqBWhw+45Gu54tmxhYa9asmRZQkez65gWo7ijDvLSO/Dsnz/CBwGb2+U2pAlSglwUdbE0VTJycwwlK7G/JsbHsp0rfibaVV7Q2sQE62/KwPViqtyvKr6bJzseKa/aW5SAJ+gpgzjZc8SY+Pji8gU39syHFAtRYfTIbNLE2mgKZoIABaiZYMcE2cFIQnk3OpSYxlcim8JzugteGDaeZkmWDaV7ybHbNYMkJPXh3OE0Jjw5HNbLhPCtP2eSezcnZZBpPIdulsIKdDb6BC8CWDcWBLDbG5kQg9ie85ZZbFF7wrLHfJpukc7xfMHGmNXVhE3vyc2wmXiVWfuNRxCMH8OAFdite8SJxyg77/7EhOqfyAGfRfl+yZMmc+rKSm7OuWdVN+S1bttSTZpiejtXeyD04AVRgjRNj+DDAew0w4OFh71Kgg70JY90HXciDdk57zrUPAion9OCVxLvI/qKxdGDwRmP24UQbPkrmz5+/hweVOnFP9AIg2faJAwDYl5Ttalhdj1eYTcE5ypKN7oMpFgxwmg2as20OG9ez7Q0byPNRgK6cpkVbsDEb0bOBOR9SeF3pY3ih2ewfjyDb8LijLSO7fG71ZA9TTvGhX6M7H2jYkgMB8ORiY+7Lhv/oyL61bM3jjrfF9vR5dKQuACB1dWfcJ/j46eWcBIXO1IlDJNzOBfQJ6upOP+K54mOSOkY+o9g2so9zYhKnfbH/Je2ItXuEazs68x7g/9nRAF1InGKE3TlogL7HOfMcBsDfqRsrzelT5Fm1apUeuwtE82zT/3N7NoN65WWbWPXjnYNWPHvYCXtEelBzA9RofW3OnDnaTg7SIPFe4jngXmjJO4x3GWEq9OdY7zVOh4v27qSPYhueY/Ti2eZIZezNbBO7fThPOdfyrKA/h0xQj1j7p+bV/6K933kOXMomLfLSyv4eLgUMUMNljwKpTSSguqlnBmheigzETCcDi9ddd52wXRJwyICIB48XNceFclILL3FOtmFTdaYlOXubvGyszskxwAdQBJRw4goDMz+AAPdic2q2FwIyKQcIcoOkE4OBmWMKOYEFAAae2biak17IxwsWqGGg5/6cLQ/IcBoU0Msxow4Cov2eU21cfQEQrmfwY9oQMABoOJEnVnvRKZgYoNmcHngAbBjEOZmnadOmOYDK4BnrPpz+gsYAAroDdmiJruiPV5LpdE4+AkBi6UC9qAuDESczPfLII2qvSEDFI84HC+1lsASsuZaTqziFh48NtAdguT8bw2O3eAD1/fffV9ClD+H5ZescbEZ8LnDI8Z98WHBv+iVgRAgJx3cCpAA+AzQnegH9eGOjJbeHZLR6MvADDcAHZXOyE3DBRw12p1/SLvabxP70ee7Fhw315sMLkOZ6TroCziiLD4mgJ3jz5s26ZVVkQq9IoHfH69JOngMHDIAK9+WH5wTbohH/5ePGPaOcMoRtI/s49eboWPo79oq1Eb0La6FsTiiiHD4+2Vif+nIAAM8o7aZP8IECJLlnAMCiH/Hhx4crbXDwRShRbn0yqE9etolWP8JIOPCCvMAe/YfTtuIFVI5njdbXgGXswZGyJD4QuQcHHfAxxUcmfZGPdfpJrPcaH8/R3p30G54lnl2e8U6dOmm93clRwCr3xs4VKlTQ09x4ZxCCxcxE5DPHta6uQU1551WpUiXnV9He72XLltW/Z4oWBTJw2k0KXAED1AKXvPBvGAtQ8RK4DeUZmBnoAAmggUGdwQtwCwIq3hUGMhIAxQueU5OCgAo0upcrgx0eFzx/DLDcB0+CmwqOBqhcB8zglWC6E68C4QN4T4BaBmsSXiiAgcESbw4DL+1hmpi64wmL9nsGY1dfgJRByB0RyUDszkFncI7W3kigpr6U40IoABy8tOjrPKjUNdp9ABwGD7e5NoMLAyODFXbDawdIcNwn3kk8kbF0IA8DPLAMqLhjPyMB1R3PySAIAJHwNuNFBugZVPkoIAyEa2lP5FR+LG8V8FuqVCkd5PG84e0NAqqLU+Z3lMsgjq3xHHJvdItnit8BarR6oiVeSOxMuXi/uA/QiN3JCzDSl7CL0wG96dN49Olv9HvACBACIABaYMMlwlp4ZiIT/S4yNpf7USf6RGSi/UAyzxz1CQKqe0Y5cShaXwaO4pnid4Dq+hm6YXM86XjJATD6F32HfkzfoX08A3wUAfScxkRfd4crAFq8K/j/WH2Sj7Zgyss20eqHp5N74XUnAcn09XgBNVZf47nIDVCpuzvCtE+fPvoxHO29FuvdiX48S5wqxXuMj0beZfzePcccWczHFB+n2Jj3L9fgbefe9DuXAFc+CiITH4K8X11ygBp8v7u/ZYoWhT+qWg1SoYABaipUDXmZsQCVwdt9eTP4AmZ84QMWDEbRABU4c1BL3BwvR85JDwIqL1o3EOPt5KudUAIGN+CRL34Sg3aLFi328KAy4AG91AWYZXAA1CLPH6cMdwa6O0WF3+FVwvuDlyra7ynH1RePjDsrnbzcmwGZwY/BOVp78XQFE+3CiwTMkoB6Bh68vg5QAZ9o98FLjQc4WswldWPwItEePC4cBRpLB+7PIMYAR2Kqj+nJSEAFRBl8g94/2or3hroA2w6wnEc0XhjIDVCDMavUjQGbKWIgmT6Ebkw9xwuoseoJbDErgDeYqW+mpoOAGoRkwMvpQF/Cc4iHlX9HJmyM9z0/ib5OyAUgGplyA9TgMxqrjycCqMGFjMAQfQMYAnzwogOuaAYMO0B1z4Dre/QNoBY9ACO8sbk9m8H2xmsb8rj68e7gQ5QPUBLvEz424u2Tsfoaz2gQUIFtNrl3HlS0d+DHOyjaew1Pf6x3J/oF+yhQzfPIs0ziA4GQD0JigO7IxCJDnsdEkwPUYN9xZWSbFolqZ9cXrgIGqIWrf6HcPRagusU7vJCZ8gUWGJiZXmbQiwaoDNDOgxkLUIEtPJEkB6gMLgx8eEGILyTh/WG6LuiRBHB4sXJ/4I34R17iQBdgS7wYgz2J6S48N/ye6Vc8ZgAVgwn/BsKi/R6vrQNUBijAzEEngy4DFZ5DoC1ae6MBKlOCLh4W2GJqG++xA1TifaPdh3g3AI1YUDx9eFYY8LgWoMOrhicTsAG0GKj5/2g6MICiDd5pBkI813gAIwEVLxA7A6AN+jHQ48WlzXjaqL+bSkwVoFI3vKX84NFk+hNIwkMZL6BGqyeDM21myhxPIHrRNvqQ86Dy8eVCD6IBKiEmeLsJ13AeZvok0BaMiSUkwvXz4INNWILzprvfx/oY4++5Aap7RpmOjtaXAahEANW1PQiA/A7v6UcffaTnwvMBQzy5A1T3DDhA5cMRHRygEiISq08Gd/UAjuK1TbB+2A6PLv8l0WbeQfECaqy+xvuIj233McY7Ck9tLECN9l4jBjjWuxP9gn0UQOV94NYCOEDFM4uGwDuJ55d3Hx/nwRAXnhN+IhMfGMGjlR2gRlucmSlaFMpAajdNuQIGqCmXOHw3yAtQCdAn9owXMHDAixSIY2oKwAxO8ecXUJlaYnBi+pQygUBe3pFT/AAqgx8xnAAaL2QGT2DBTZMygDJYAhHEszIdSwwYIIb3F0AF+ICEaL/H4+MAddq0aTrgMU3HgIX3lPvhrUwEUFlsRfwoXg/AHdjDa+oAlQEo1n2wD21m4MKbQpvQHJsAUGjGvxkIiU8FoJgujtSBQYqpfUIj2rZtq+1H60hAZQDEzgyueDGJbSVGDu82g2SigArEMvDhDY/Xg4q3Dr2wMwCJnViFDpDzwcEAjTczVqJfRKsnH1V49wiLAHaJneY62siuAtg9L0DlAwR7oA8xieQF6CPrBADg3YtMQEXkFm9BQAUg8Jhhd/prPICKJz1aX8ZLj6edDy0WGMVKboo/GqDiXUYvPh7pA/Q32sDUevAZiAWogFisPkm9XMI7Hq9tgoBKf2VhFO8MYpeZXcEmuQEq/ZFnyW23F62vEecO3ONJxWvO/1PHRACV2NtY706ei3gAlf7Au4x6AKX0Oz7M3QItpx/tdwu6gnbmo4J3tkuRgJqJWoRvlLUaJUMBA9RkqJhmZcQCVLc/Jy96pupZJEACzvCgAGtAES849kGNBWxuip/pQ6axIz0N8+bN05WxeGgALu6Dt3Dr1q26ijXyLHa8GCzAYjADOJi2BoyZsgVa8SSSWCADsAGBeH6BYPLgNSPEACCJ9nte6IAK9cXzw+BA2SQGCAZ9ykkEUJniJC6TxACKN5pE3dAZCIt1H6YsXdwnkEz7gSvAg7AL4IjFTtTHrWyPpQNeIKCKxGIl2gVAOK+167oMgHgt8RAyrQugALWxwC/anpPEXxK/iU1fffVV/ShgNTdeUQdCLgaVhTjueur51R8RAAAgAElEQVSA14oPC2xJHagfg+/++++fszAnt62mcqunWxlPufQB7o0GfOTkBahMqRJjTb8KnokO9LP4Lb8JQKVf8EFAnyYmkY8jYBBABeTwCkbGoAb30I3Vx/HU02Y+vGJtNRULUIFSPmpY+ET8KQlAZ1EYH0AAfqQH1cU4A+F8YOCRz+3ZDGoWr20coFI/bELbicMmNWrUSGE1tz7pQnVYdAd0RutreDDpk8AufQUI5gMTQMXefIDwPiPx0RvtvcY7J9a7k/diXoBKO9hmjAWUzrPKuwddI/dVjrfvRe6/nM1axKuZXRcOBQxQw2GHUNaCaV0AgUGclzo/8e5fGU+D8GgATcAa4EaMKCCMByIy4ckBXvBsBE8j4uXL1CpgCcwFEyANIAW3VOHvsX7v8jL1iMcSrxGhAfk9EYo64+0ByqKl3O5Du/BQxXv/3HRghwVWmOOdy60tDN5ozHWFsSk+3lZAFo9pcDEI2jGdzSIRvPiRiTYBHLmloC0AGaalY9klVjncm37Bx4eLm46nn6fymlh9mfbyseAgM7IOPEc817ESGgFmzHLwvNF/eB5Y8BVvyq1PBsvIr23YkYEFeJF9JZ765dbX+HDhvRdrx4h4yk/GuxPb8eGGU4B2piqZFqlS1sr1VcAA1VdBy59vBYjDxEPBwgE8WnhUiS0rDDjKdyMsY4EpgNcesIwGqPGehFZglQ3BjYhlZLYiWsI7my0HH4TAFFYFU8AUyIcCBqj5EM2yJEcBvDJM4zKI4uUk7pHpLEumgClgCpgCpoApkN0KGKBmt/2t9aaAKWAKmAKmgClgCoROAQPU0JnEKmQKmAKmgClgCpgCpkB2K2CAmt32t9abAqaAKWAKmAKmgCkQOgUMUENnEquQKWAKmAKmgClgCpgC2a2AAWp22z9rWs9epmytY8kUMAVMAVPAFEhUAbbfs/T/CrB/MymRrecS1c8ANVHF7Pq0VIATlLp37x7aurP/Z5hfgFY/v65j+pl+fgr45bb+Fx793HHR1IiDH9jyjT1v3Q42nPjH6YGxEoeqsB852zPGShx+w4EunFjYoEEDv8bHyM2pa+yNzrHjsRJ7HHNKIe2JPO6ZXXw4QY8T8dgPPVoyQE2J6azQsClggOpnERvgTD8/BfxyW/8z/fwU8MudzP4XBFROZeMENgeU1JLTAjm9Llbi8JYqVarIG2+8EfMatm/khDV3Op1f66Pn5pRCDuPglMdY6ccff9ST6TgCmFPjgumbb76RSpUq6e/5uwFqKqxkZaaFAgaofmZK5gvarybRc1v9/FQ1/Uw/PwX8cmdT/3OA6o7hxgOJh5GDSDjJzwEq4IqHkZMA69atq0deP/nkk3rMLkfx3nbbbXqstLsObyZH9XIsL4d0AKjXXHONHotN4rji4NHHHNXL8eD8cGzzBRdcIH/++aeCL/CJd5fTHpnKf/zxx+X555+XypUraz04mpyyOamRY6A5BrhDhw7qTeWYZsCYY31btGihgMoRzrSDk+84Qps6ctQ3xwpzAuSQIUO0HpHJPKh+z5XlThMFDFD9DJVNA4ifUgbQpl8qFPAr057f8OjnAJWp/eeee06n9xs3bqzHCgOFACpwd/jhhyvEXXLJJdKpUyf9ueyyyxTkKlasqNBIHo7CBfIuvfRS/d1DDz0kxx13nAIqv2/Xrp0MGjRItmzZokdvB48KxxsLKLNGw50sxzG9wCdHiPPvc845R5YsWaKe0pEjR2p9OXb67LPPlq1btyqUAsavv/66NGvWTObPny+ff/65htRxwh+ASurZs6e88sorWtaHH36o4EyZtOe+++6TE0880QA1VjflPGK+QDgXPkwJFzl1Cnaqgqof7vu99tor32fR+9aTh6l48eK+xWh+A1Q/GW2AC7l+b14usuqVfFfyq3+usBjofKsnYs+Hh3iSXfo5QMVLefPNN8uECROkadOm6gnl+G8AFbDDk/nJJ58oKI4dO1bjNDkSPDjFP3jwYPWkAoScxggk7rPPPrJp0yYF1BEjRsh1112nwIuH9ueff1bvq0udO3dWb+m4cePklltuUW8pENq8eXMt9/bbb5dDDjlEzjjjDDnvvPPk7bfflnfffVfv06tXLwVUToKEUQBhPLzA5+mnn74boF511VXqgX3mmWfk2muv1fwsWk7bKX7cz9B/MEH1GO/II4/c7fcTJ05UQ/znP//J91OCofhaufjii6OWQWepUKGCnHzyyXHf46mnntIvHkCPvLjPixQpEnd+LnzkkUekaNGi2pGTmejAuOZxy8dKuPPpUHS2ZCceBr4CY8WerFu3TjXbtWvXHrfOjy0MUP0saANwyPUzQPUzkGduez78BMwm/Rygjho1SrmF9MEHHyiMMnUO4zDdzrjrptKJSYU9IgEViARSv//+ex1PmWrHqYOXMhiDeuONNyoAM+4HnXCAIt5PAJRFugDq+vXr1bs5Y8YMKVWqlNSpU0fhGE+qSxxLDmgCqADrQQcdJLfeeqvWZcGCBVKtWrXdANXFoMIcwC8hAHhM0x5QcTGXK1dOWA2G+/jMM89Ud3Uwbdy4UQCa448/Pl9PCUblC+azzz6LCZCtW7dWIxLUHG/CaG+++abW69NPP5VatWrFmzXnOr54+DKibgceeGDC+WNl4KuMzk/5hQGoxKLwILlphcg65Aao+bGFAapf18mmAcRPqei5U66fAWoqzBZ3mSm3b9w1KaT+Z/WLW4EgoC5btkwGDBig3kSgjSl7AJVxEc8nU/5AIrGjQQ/qvvvuq9Py9Lv69evr3wkTIOYTUAQm4wHUbdu2SZkyZbTuxLsCqHhvScwo83dW4RMyAFCPHz9e5s6dK5MnT9ZdAtwUP/fDu8qU/UcffaTQGpzizw1QGc8ffvhhvU9kCm0MqvOgAp9sqUDCFY2bmgBgjEJsBkG2GJRgXgJtMThBusRBALS4qFnxBvFXrVpVRo8erQK7uAjK7dq1q3rr+C9T/RgYryz3QPCVK1eq+5r9vnBTcy2Cv/POO3LWWWdpB+PrJngPPLHUl68SPLtsCcF9uZZpewxI5wJ4qT9bLuAlpW14cvnSwWh0WNz+QCpfS8EEdLESji8W4kJoK6v3cLlTF7yvdCC8w3R+vlbofHTm0047Ta+jE6MdW0AMHDhQpw/QgSkBPKi0j3aSCI5Gc5eASLaw4KuOEAS+tCiHthPIDZjz0BAAzhQCsS78G68y96JD0jnp7MSnMDVBGdgbzy46oz+aU3emOQD9oC2wUTzJADUelWJfYwNwyPUzQPUzkGduez78BMwm/YKAyljHGEl8KeMu4z1jKFyAA4lFU8Ahs7CM5bAIC6OIMyV2E2AkP7sAkOAQvKJAImM7/8Ybe9NNN2l8aqQHlTwufhQ+gX+ILYWd8HaSXn31Vb0nHl4SYzcsUqNGDYXYxYsXy6JFi+T+++/X+7FtFHlYeAWTwFr8m4VRL730kpYNjxAywFZVeHuBY+qYdoAKlED4ACtG4asCeNl///21oYDNihUrZPjw4RqzAfwQNMwUMoBG4xHDiYkL+txzz90tphP6p3MAVgQtA7GAHvkoY+bMmRo0DNQBe0AaRuQeQDGgB9AG74G3FagE/oBU3O9M8Tv4pWw6H50DYB0zZoy61WkjdcClj+H5smLfM8rHGxtM3J8OSqfgp3fv3jplgJcZGMdL+vLLL+v0AXEldCICqbkf/6YjUQeuozMDoqy0q1evnq62ow18yZH3xRdf1A4LDAcTEEv9+Mr7xz/+IUwl8P+ALg8N+qMhmqJTq1at1F78Dk3o6HRgyue/gDwPJjDsABVwp6NjVx6CoC3ijVE1QLUBxE8Bv9wpH4ANUP0M5Jk75fa1+nkq4Je9MOxLeBvcQwxoZGggYAhPlC5dWhu2du1ahUsWViUaRhivMjjDGKPdPV0+6oEzjthWxneciHAaHJPbXq3kJy+zuJRZokSJ9ANUvhKAECCVLwW8i6xGA1Bd3AXwBqASbEx8hftKYAuEkiVL6ooz4BOKZyo/mOgE5OHrAU+rC/DliwbvILCGeMFpZe6P5xaww81OZyEfgBq8h5viZ2rexaACqNQdDyOJawBrvh7wruKxxM3OPR2g8ne8hoQ7RAIqbSS2hPvjTcTjzMKmo48+WqEXyJw1a5aCIoDIlAH1p0O7KX6+2Ojc/fr10+JZlQeY1qxZU0GZTsbv8IhGxoTixgdO0ZmHGA8w9aUd3AP74XkGhkkAJvueURcAlXvQbrenG39buHBhDqB+/fXX6j0lnhfgxdY2xR/vKyV51xXGCzqR2md9/QxQE+kuSb826/ufp6Kmn6eAhZydmVHGdpdwyuFwg3t8UlpN8buGAmFAntsywQEqLmy8lu73DvQAJTyVeOXw8gWTg1hCA/AGkphGxsNJucAe8BeEIncyA1PlxMbipXSAGrxHLEB10+gOUAkBYJ8zPKQuTpUYEzyfeFABTMIWIk9swIPqygL4CD7GO0lygIr3lylypgzoNHiYIwGVtgGfbsES4E/cL4HObpEUMI62kYAKkNI5AVS8v+x3RkgCXlq+qnDhA8LB+FtswGo/AJUpDiCfH9ITTzyhsSzOg+rux1cZXm0DVJ/HPf95bQDJv3bkTLl+Bqh+BvLMnXL7Wv08FfDLHnb7+rUuOblxJsFOOOROPfVUDdnzTRkFqHjhWBmGR7BJkyYa1Mv09Jw5c9S7GQ1QERDIwwOLl5WvAFzN3bp1Uw8gEAVsMcUMsLVv3169uSysAuqARMqPBahMveOiD3pQowEqIQYYlc10OZ6MAGTnQWV6nBV1gGYwxQOoTKHjbucHuEMXB6gAJ7EtxIACl9yXWFg0JNYFL2hegIrn1S3e4sOB8Au8nQAneuIdZQsM9EVHwisokx+0JeYUYKVzA/R4gdEnN0AlPhZb0P54k03xx6tU9OvC/oLO+voZoPp1cM/cWd//TD9PBSx7NAXSGlDdPp3BbaaY5sezyYIctktwQcIAKnGPAFFkAqrwngJzeCmBJKbYKZ94RwKEAV13QgLeVWImAVWuZQEQHj6myIP3YGqcYGE8oUCfi0ElTgTgdXAMzAHFTJfjPSTQGfe429sMzyYeY6A5ElBdWQRFszAq6EEFAgE/PJpAH20EeomhJWCZkAbgGQ8uMPnxxx/rdDzT+oQccD0hAcTexvKgUh9igUloQkwsKwkJ6OYLCm8scalAPPZwW1EAl4QR0GbgmFhevNlsLYZW/C64zVTQgxq0BR7beJIBajwqxb7GBuCQ62eA6mcgz9z2fPgJaPr56ZepuUMLqD6CA0cEF7MijqnnvBLxp27lPwHGgCkeUhblAGwu4V0ESpn2x7vKNDwgy1YMrGKPvBfT08TDMl2eVwISmZanzoQNAGcAG/UB2JiqBybzk/CY4ukkwJnwB0IfqDvA/Msvv+RsM0GsLt7QVBwKAKhSLh8OwcROAEC5iw3Gk4wnNdb+qNFsEY8mBqjxqGSA6qdSIepngJoq08VVrgFWXDLFvMj089MvU3NnJKDmx1jEYXL0WLzbFuXnHrnlwfPIFlBM7TOVz6Iodhzg93hS8WpmYuJjAABnyh4PK15c4mldPHB+2sy2V+784WB+vLVhTfaC9rNM1utngOrXgTxzZ33/M/08FbDs0RQwQP2fKnhH+WHKvLASC7UIMuakLKbVOUGKqXWmwiO3diisOqbivni8gUp2Q2CLLDyoyU7mQfVT1AbgkOtngOpnIM/c9nz4CZhN+pW8N7HTJCOV/e3u3U9XxMnDbGuQEZgdZaY0GQuV/Czrl9sA1U8/y50mChig+hkqmwYQP6Wi5065fgaoqTBb3GWm3L5x16SQ+p/VL24FDrrfD1DX9f4LUHfu3KknTLIombBDZlxJLKpmkTEhgcArM7LJPIUy7oYm4UID1CSIaEWEXwEDVD8b2QAccv0MUP0M5Jnbng8/AbNJv8oP+AHq8p5/ASozj4T+sUCaReAAKkDKvvFuL3gOFmK3HPZnT8dkgJqOVrM6J6yAAWrCku2WIZsGED+lCsmDZYCaCrPFXaY9H3FLFfXCbNKv5oP/D6igZm64Gu3vH/XYfYqfkxsJBQRQ2R6SdTRuNx/2IufYd7ys6ZgMUNPRalbnhBUwQE1YMgNUP8kKVj8D1CRaK/GisgmwElcn7xzZpF+dAQEkzQehzu4WG1A5hZFtNt2hPmwTyUE/HIyTjskANR2tZnVOWAED1IQlK1jA8qte6k9qCnv9DFA9LeSXPZsAy0+pQpph8Kx0Mu173kN+U/zT74gNqO70TBZJsUWli0vt0qWLpwKFk90AtXB0t7sWsAIGqH6CJ/MF7VcTG+CiKmCAmopuFXeZ9nzELVXUC7NJv0sG+gHqq7fHBlTE5Xhxjn2vWrWqnH/++XLPPfeIO57dz0oFn9sAteA1tzsWggIGqH6iZ9MA4qdUIQG0AWoqzBZ3mfZ8xC1V1gNq08F+gDqhy56ASszpoEGDVNspU6bonu4kTogcO3aselPTMRmgpqPVrM4JK2CAmrBku2WwATjk+hmg+hnIM7c9H34CZpN+Lf/tB4vP37o7oEZTnhMi2dedFfzpnAxQ09l6Vve4FTBAjVuqrPdw+ClVSB5Uz0pnEyB4SmXPRwoEzKb+13aIH6CO6pw3oKbARIVSpAFqochuNy1oBQxQ/RTPpgHETykDVNMvFQr4lWnPb3j0u2GoH6AO62SA6mdNy20KhEwBA1Q/g9gAZ/r5KeCX2/qf6eengF/uZPa/Wx/z2wf13zcZoPpZ03KbAiFTwADVzyDJfEH71cQ8lKZfKhTwK9OeD9MvXgW6D/PbB/VfNxigxqu1XWcKpIUCBqh+ZrIB2PTzU8Avt/U/089PAb/cyex/fYb7TfHf09EA1c+altsUCJkCU6dOlRNOOCFktbLqmAKmgClgCqSDAkcddVRSqvnAE36A2rODAWpSDGGFmAKmgClgCpgCpoApYAr8pcDAEX6Aevv1BqjWl0wBU8AUMAVMAVPAFDAFkqjA0Cf9ALVTewPUJJrDijIFTAFTwBQwBUwBU8AUGPmUH6Be184A1XqRKWAKmAKmgClgCpgCpkASFXhmlB+gtmprgJpEc1hRpoApYAqYAqaAKWAKmALjR/sBavM2BqjWi0wBU8AUMAVMAVPAFDAFkqjApKf9APXy1gaoSTSHFWUKmAKmgClgCpgCpoApMG2MH6BedK0BqvUiU8AUMAVMAVPAFDAFTIEkKvDmM36AWq+VAWoSzWFFmQKmgClgCpgCpoApYArMftYPUOtcY4BqvcgUMAVMAVPAFDAFTAFTIIkKzPME1FoGqEm0hhVlCpgCpoApYAqYAqaAKSALn/PzoJ58tXlQrRuZAqaAKWAKmAKmgClgCiRRgeWegFrZADWJ1rCiTAFTwBQwBUwBU8AUMAXk6+f/34OKLzQ3f2q0vx/RcncP6rZt22TvvfeWokWLZpy6RXbt2pU9/uKMM581yBQwBUwBU8AUMAXSRYE1noBa8X+A+sMPP8hVV10lxYoVk1WrVskdd9whrVu3ThcZ4qqnAWpcMtlFpoApYAqYAqaAKWAK+CnwUwBQ81PS/v8D1P79+8uWLVukX79+snbtWqlYsaLgTS1dunR+ig1lHgPUUJrFKmUKmAKmgClgCpgCmabAL2P9FkmVvuqvSe/27dtL/fr1pXnz5sJEOFP8K1askKOOOipjJDNAzRhTWkNMAVPAFDAFTAFTIMwK7PQE1L3+B6jNmjUTfpo2barNPeigg2Tu3LlyxBFHhLn5CdXNADUhuexiU8AUMAVMAVPAFDAF8qmA5yp++d8q/nvvvVf23XdfufXWW2Xnzp1Srlw52bRpU0YtljJAzWcfs2ymgClgCpgCpoApYAokpMAYvyl+ufavKf4pU6bI0KFD5Y033pCXXnpJBg0aJHPmzEmoKmG/2AA17Bay+pkCpoApYAqYAqZAZigwyhNQ2/4FqNu3b5eLLrpIlixZov+ePn26nH766Zmh0f9aYYCaUea0xpgCpoApYAqYAqZAaBUY4Qmo1+++M+jq1aulQoUKUrx48dA2Ob8VM0DNr3KWL60UmDp1qpxwwglpVWerrClQUAp88cPcgrqV3ccUiKrAEeWqSfG9SoZWnaStjh/uCagds2fregPU0D4OVrFkKvCvf/1Lunfvnswik1rWV199FertQax+fuYOu34XD/QcNP3ksdymgEy6ZbuUKFYqlEok9fkd6vmsdTJADWUnsUqZAvlVwAA1v8r9lS+pL2i/qkTNbfXzE9UA1U8/y+2vQNYA6hBPQO1sgOrf26yE0CqwcOFC4Zi0YCKGJZ2mwL/99ls9RaNKlSpx6WyAGpdMMS8yAMxs/QxQ/exruf0VyBpAHeQJqLcZoPr3NishtAqwse+ff/4pVatWzaljtWrV5PLLL/eq83PPPSc7duyQNm3aeJXjMtepU0dmzZole+211x7lPf3007p6ccCAAXHdywA1LpkMUP1kSlv9DFBTZHgrNm4FsgZQH/IE1DsMUOPuVHZh+ikAoHI8mjuBwrUAwGRvtRdeeEG3rGCPtUmTJsno0aNl8ODBUqxYMenSpYu0bt1a1qxZoyD66aefSsOGDaVbt25y9tln63YXEydO1CPYSN9884107NhRypYtKx9++KF07txZy543b56eIcxxbe+995706NFDVq5cKVdccYXu59a1a1cZOHCgNGrUSPd4u+2222TMmDF6j0cffVTB9dlnn5VffvlF8KY+/PDDcuWVV8Y0hgGqXz81D2pm62eA6mdfy+2vQNYA6oOegNrDANW/t1kJoVUAMP3555+lcuXKOXXs0KGDHHPMMXLiiSdKz5495YEHHpAhQ4bIWWedJZUqVVKg5KzfGjVqyIYNG6R///4Kh4Bju3bt5Prrr5f58+fLb7/9Jn379s3Z8uLLL7+UY489ViG3VKlS0qJFCxkxYoQceuihmoctMk477TQFXKC2bt26MnLkSK1HmTJl5Mcff9RyKXPChAkybNgw2bZtm4YjAMgc7bZ8+XKtz+LFiw1QU9TrDFD9hA27fgaofva13P4KZA2g3u8JqL0NUP17m5UQWgUA1AMOOEBq1aqVU8cGDRrI4YcfLjNnzpR69erpGb94Unft2iVAJl7ORYsWqXdz48aNes3YsWMVctevX69T++PHj5dff/1VevXqlVMueatXry4//fSTwiaxrlzLT+nSpbV84mHfeustBU2m7MeNGycXX3yxFClSRK/r3bu3HHfccdK2bVuF4lWrVimYvvLKK/qzefNm2W+//bQs0uzZs7W+kclW8ee/S4YdsKx++bctOQ1Q/fSz3P4KZA2g9vUE1L4GqP69zUoIrQKxpvip8Pvvvy+1a9fWaXy8nsDjgQceqFs01axZU6fggU28qsuWLZODDz5YofGPP/5Qz2c0QG3SpIksWLBAPa8nnXSSrFu3TkMBAFRiYSkX7+2FF14oo0aN0nsFAfWqq67S+/LDfThvmH1NXQwqHlW8rQ5QowlvU/x+3dEAMLP1M0D1s6/l9lcgawD1bk9AvdcA1b+3WQmhVQBABfYAR5fwVu7cuVNOPfVU6dSpk0Li5MmTdaU88ad4OIlPJSYUQCWWFC8qINuyZUtp3LixfP3117J161bp06fPbh7U3AB17dq16lUFcimXqX/g0wEqIPv8888rOBMaAMDiycUra4BacF3MANVP67DrZ4DqZ1/L7a9A1gBqD09AfdAA1b+3WQmhVYDpexYeBRMr+IkFZQHTjBkzdPr+7rvvljlz5siZZ56pIQElSpRQzyeQysIoFj+xWIqtnqZNmyZvv/22gioLq4BXElP8uQEqXk+AeenSpXo98aqA6jvvvCOs4sczyuKtG2+8UeuC1/W1117TaXwD1ILrYmEHLKufX18wQPXTz3L7K5A1gNrNE1AHGKD69zYrIWMUYBoeTyfT+UyxM40POJKYbif+0yUAFpCNtjVUboIAuuXLl9d8xLiWK1dO7/X7778rlEa7VyIC2xR/Imrtea0BYGbrZ4DqZ1/L7a9A1gBqF09AHWyA6t/brARTIEQKGKD6GcMANbP1M0D1s6/l9lcgawC1syegDjFA9e9tVoIpECIFDFD9jGGAmtn6GaD62ddy+yuQNYB6gyegDjNA9e9tVoIpECIFDFD9jGGAmtn6/ffDcXJwxYp+jUxh7u/XrLH6eeibDvqdU/1yKVGslEcrU5c1qe+/6z0BdYQBauosbSWbAoWggAGqn+hJfUH7VSVqbqufn6gLPhknFQ8OL6Cu+X5NKOtXvsI/VXjrf379L6v0a+MJqKMNUP16m+U2BUKmgAGqn0GyagDxkyotAfqF0Z6DZgo0C3uR/6g/RQ4+7BID1CQYKqveL608n7VnDFCT0OWsCFMgPAoYoPrZIqsGED+pDFBToF8YizRATZ5Vsur90twTUMcboCav51lJSVGAbZh+/vlnPVq0oNK3336rG/Wzz2ki6fvvv9d8BVHXyK2oYtXTADURC+55bVYNIH5SGaCmQL8wFmmAmjyrZNX75QpPQH0pPkBlO0i2bSxevHiOodgykq0g99lnn+QZL4UlFdmV2/mQKbyxFZ2YApymxPnzQ4YMSSyjx9VPP/10zmb4wWJuu+02ueaaa/TUqWiJU5++++47Pa0qVYlTrz777DN56qmn9CHktKvckgGqnyWyagDxk8oANQX6hbFIA9TkWSWr3i+NPAF1cu6AytHfnLbYrVs36dKli3AID4mjy//973/LIYcconuMwxQcYx7mZIAaZusE6hYEVE5SovPRye666y65+uqr5YILLpAHH3xQofHTTz+Vfv366WlR/G7YsGF6hOhjjz2mR4RyytMZZ5whTz75pNSoUUPGjx+vx5zecsst8t///ldPjgJOX3nlFXn22Wf1GFK8qQ8//LDWqHnz5lreggUL9HSnyLq8+uqr8sUXX+gJURyJuvfee8vMmTO1ngMHDoypOJv+c/Ygr4IAACAASURBVGrUcccdJw0aNMjVMhypyklXH330kbbBADW1HTmrBpAUSBl2/SwGNXGjG6AmrlmsHGF/PpJav4s9AfW13AGVcXnMmDF6VDljNoAKK+BJZYwtW7asjssVK1aUHj16JM+IKSjJADUFoqaiSAeogwYN0i8goBIX/pVXXimffPKJPP7443qiE9DWp08f9Spedtll0qJFC3nxxRf1CFPgcNWqVdoxW7ZsKTfffLNCZO/evfWkKL66ANvu3btL3bp1ZceOHdKmTRv13C5fvlz69++v97rkkksUZoFIQDWyLhxDunDhQj2elKNLuf8RRxyhR6muW7dOT4wKJsoePny4ekOpMyALZAfT3//+d2nXrt0e0gLdHKdqgJqKXvf/ZSb1BZ2Cqlr9/EQ1QE1cPwPUxDUzQBWR8zwBdXp8U/yM7YzzAOrKlSulfv36smLFCjXBo48+KvPnz9cxN8zJADXM1oniQQVIW7VqJb169dK/Amh33HGHHH744XLDDTeoV5PYz0mTJukX1HvvvSeNGzfWawG8b775RmrWrCkff/yxwmXPnj31CNNjjjlGmjVrpt7QSy+9VE4++eQcLyqe1M2bNysAExHSsGFDhdiiRYtGrQvg7AD1/PPPz3koANQnnngiJzRg6dKlWnfq3KlTJ2ndurUcdNBBsnjxYnn55Zf3AFSANzJFA1QAmXZHJuoc1mSA5WcZ089PPwPUxPUzQE1cMwNUEanrCaiz/gJUxtivv/56N0lxBDF2k4KAyrWE3OEMIjEzOmvWLJ1FDXMyQA2zdaIAau3atdVL2rFjx5y/MmV/4oknKmjiacW7CeQBdCxYwhvqEhB4/PHHy7Jly/S8e7yteCfxpr755ps6rc/0wKhRo4Rp9CVLlsiAAQOEuJYyZcrsBqiUHa0ueFkdoDZp0kQBlFSnTh39cnOxq4QqALvEyVBXIJkEDK9evXo3y5QqVSrn78E/mAe1YDqwAaCfzmHXzwA1cfsaoCaumQGqiJzhCagf/AWojNGE5AXThRdeqI6eSEBlYVTp0qWFRVJFihTJmXFk7A1zMkANs3WiACpAWa1aNcH7yDQ+AHjfffdpHGbXrl3Vc9qhQwf1hE6dOlWeeeYZGTdunC5aovMydX7YYYftAag//PCDloH3FOgDTGvVqhUTUFkodcopp0StC6EC8QAqzduwYYN6aln8VbVqVbnpppvUM3vrrbfuZhm8wlOmTNnDWgaoBdOBww5YVj+/fmCAmrh+BqiJa2aAKiLVPQH1k8Sn+NGd8ZpQQMZZZjbvueeePNd6JM/C+SspawB13rx5OvW97777RlXqjTfeCLWx8Ix++OGH8sgjj8i9994rI0eOVE8j8aAsLOKr6IMPPtAFTsSZHHXUUbq4ia8ppvMZwIcOHaoAyDS686D27dtXPahMvxOjwvTAxo0bdQoAyIzmQaVj09GBZMqMrAse2HgB1RmDIO5p06ZpeewQwE88CUClvcTm5pZsFX88asa+xgAws/UzQE3cvgaoiWtmgCoix3oC6hfxAyrrOVjXQcLBw/+TLr74Yhk7dqxyQ5hT1gDqRRddpAuAYm2NhKHSacct9kRlCp5p/bwSC6BKliypU/q5JYCWafujjz46z47L/R3sJ1KXvOrK34HVYsWKxXNp3NcYoMYtVdQLDVAzWz8D1MTta4CauGYGqCJS3hMK18cHqNG0ZozHscVC6XRIGQ2oLMghfhLPKavX8TTi3mZhDqvXjjzySF20w5ZLAGrbtm11aya+Lvg7MRuWMkMBA1Q/OxqgZrZ+BqiJ29cANXHNDFBFpKQnoP6Wf0BNnsUKpqSMBVRiG/EETpw4UYivZLslFu8wfX3nnXdqbCaLdJhWZnEQgMoqb6bE27dvrxDbqFGjgrGC3SXlChig+klsgJrZ+hmgJm5fA9TENTNATZ5m2VBSxgIq2xSx/+YLL7ygdgRWJ0yYoHt2shG821PTbWvEiQpMb+P6ZnUccZvEN1rKDAUMUP3saICa2fot+GScVDw4vNN+a75fE8r6la/wT+0Y9nxk9vMRdvv6qR/e3BkLqMAlC4HY1ojECnX2/GKzebypLnCYOEo2egdUf//9dz1tgQVJ7KPJQiBLmaGAAaqfHcP+gs6a+q1528+QMXJ/tf1wXVgZ1pQ19k2RAUw/P2HDrp9f68KbO2MBlQ1p2TKJle8s4mEje6b4OT0B+CT2lFXv7L/JPp1M8c+YMUP3DGUFOZvcsoWTpcxQwADVz45hf0FnTf0WDRL54HY/Y0bJ/VW9v3b+CGvKGvumyACmn5+wYdfPr3XhzZ2xgIrkwCcLozgLnlOQmN7n9CSm99kPlPPl+R2nMwGobLHEineu4ex4zqy1lBkKGKD62THsL+isqZ8Bql9HTlHurOl/pl+KFLBioymQ0YBKg9evX6/bIXESUTARbwq0Blfqs83UmjVrNA41bPuDUS88wexIUFAJgN+yZYtUqVIloVuiLfkKoq4sesO+HFqQWzJATciEe1xsA3BI9DNA9TNEinLb8+EnrOnnp1+m5s54QM0UwxEXO3fuXD1xqaASJzy5jfqD9+QUKcIgYu0p+/777+vJVZz9m6r0zTffqOebxW3smVq9enXp3bt3zNsZoPpZwgaQkOhngOpniBTltufDT1jTz0+/TM1tgJomlg0CKttjcZQpG9rfddddejrEBRdcIA8++KBCI+ELHErAnq78btiwYRq2wMIxQK5evXq69yuLxlg8RpjDzp075ZZbbtGzfTmNCjh95ZVX9EQpNvfFm/rwww+rWs2bN9fyiN2dM2fOHnV59dVX5YsvvtA43s6dO2uIBSET1HPgwIExFd+0aZOGZLDLQoMGDXK1zP333y87duzQ49p+/fVXvQdQHOvgAgNUv45uA0hI9DNA9TNEinLb8+EnrOnnp1+m5jZATRPLOkDlSM9DDjlEoRIww4vI4i92HCBk4e6775Y+ffrolDc7FbBjAdttTZ8+XeGQAwsIYWjZsqXcfPPNCpF4Hom9XbRokYIt+8GyWAwAbNOmjXpuWXTGDgjci+NVgVkgElCNrMvs2bNzjjo99thj9f5HHHGEHqe6bt06KV++/G6qU/bw4cP1cATqDMgC2cHEcaxuazB+v337dg3DIHRj8uTJgleX3RhihWYYoPp1dBtAQqKfAaqfIVKU254PP2FNPz/9MjW3AWqaWNYBKkDaqlUr6dWrl9YcryiHCrBLwQ033KBeTWI/J02apODGjgWNGzfWawE8psZr1qyp+7wClz179lSv4zHHHCPNmjVTbyi7H7BgzHlR8aRyPBoATJxuw4YNFWKLFi0atS6A88KFC3WR2vnnny8rVqzQ+wOo7J7gQgOWLl2qdafOnTp10kMS3L607GMbCaiUF0xsC4aHGPCmjueee67+GUCm3ZGJOoc12QvazzJZo58Bql9HSVHurOl/pl+KFLBioylggJom/cIBau3atdVL2rFjx5yaM2V/4oknKmhyHd7NxYsXKyCyYAlvqEtA4PHHH697xJYrV069rXgn8aZyohagN2bMGD2sYOvWrTkxqNu2bZMyZcrsBqiUHa0ueFkdoLJVFwBKqlOnju5L6wCVUAVgt0uXLlpXIJkEDK9evXo3y+ApdX/nD0AwQF2iRAktM6+zhc2D6tfRbQAOiX4GqH6GSFFuez78hDX9/PTL1NwGqGliWQeoAGW1atUE7yPT+ADgfffdp7GkXbt2Vc9phw4d1BM6depUPdJ13LhxGp954YUX6tT5YYcdtgegchwsZeA9xSvL4qhatWrFBFSm1E855ZSodSFUIB5ARXqOpMVTy+KvqlWryk033aSe2VtvvXU3y+AVnjJlSs7vRowYoe0L/i43Uxqg+nV0G0BCop8Bqp8hUpTbng8/YU0/P/0yNbcBappYFkDl0IFHHnlE7r33Xhk5cqR6GokHZWERsZccPMACJ6bU2XSbxU14TJnO5wUwdOhQBUCm0Z0HtW/fvupBZfq9fv36OrXP1k0sjgIy3Sr+oAeVhUnEvALJlBlZFzyw8QKqk58FX9OmTdPy2CGAn9wSsbGAbTB9/vnnQsxrtGSA6tfRbQAJiX4GqH6GSFFuez78hDX9/PTL1NwGqGlqWfZEZQo+1qr1YLNYAFWyZEmd0s8tAbRM2x999NF57gPL/dl/lJRIXeKRG1hl66hkJgNUPzVtAAmJfgaofoZIUW57PvyENf389MvU3AaomWpZa9duChig+nUIG0BCop8Bqp8hUpTbng8/YU0/P/0yNbcBaqZa1tplgJrEPmADiJ+YSdPPANXPECnKnTT7Wv1SpIBfsWG3r1/rwpvbADW8trGaJVEB86D6iRn2F3TW1G/N236GjJH7q+2Ha9x6WFPW2DdFBjD9/IQNu35+rQtvbgPU8NrGapZEBQxQ/cTUF/R+xUQ2fOVXUIpyr1mzJs+txlJ067iKDXv9vipugBqXIWMBPs+HAX6+JQw7AIa9fvkWPuQZDVBDbiCrXnIUMED10zEHULtX8ivIcodSga96/LXzR1hT2AHB6ufXc0w/P/0yNbcBaqZa1tq1mwIGqH4dwgDVT7+w5zZA9bOQAZbp56eA5Y6mgAFqCvoFx4G+9dZbupH9AQccoHdgio/tmCpVqiTz58+XM844I6E7Ux4nMRUvXjyhfGG7mK2s2CM10fb7tsMA1U9BA1Q//cKe2wDVz0IGqKafnwKW2wC1gPoA58ADk5z6xEb4JHcS1O233y4NGjSQ5cuX51kbNuDfsWOHsCl9cHP9PDOG+IL169frxv69evUq0FoaoPrJbYDqp1/Ycxug+lnIANX081PAchugFlAf4KhRPKXDhg2TVatW6dGdeQHq6NGj9Vx7ElBbt25dqV27tmzfvl0mTpwoLVu2lMaNG+u/OYKU40v/9re/CefZc6wpm9vfddddcvXVV+tJU4Dt2LFjFYTdpvd4cYFdjjtt2LChPPHEE/Ljjz9Ku3bt5J133lGoHjVqlPz+++/SsWNHKVu2rJ5e1blzZ5k+fbrMmzdP+vXrJ+3btxeOOt25c6ceN1qxYkU9eerII4+UJ598UgYPHqz37NKli55kFazPjBkzpGfPnlr/8ePHy4MPPqgHDvTv31+uuOIKvd91110nK1eulGuvvVbL4h5vvvmmfPTRR/Ltt9/KgAEDpEWLFlHzxzKxAapf5zdA9dMv7LkNUP0sZIBq+vkpYLkNUAugD3AkKIsNvv76awU+jiblv7kBKoDG6U0vvPCCkL9t27by5ZdfKpz99ttvCqyHHnqoXHbZZdK1a1dp1aqVdO/eXSHzkEMOUVD79ddf5corr5RPPvlEAQ6gA5Dx1rrEEaVMsQOXQCkQyjGoq1evVmjE41ulShVp1qyZHhkKNJcqVUphcMSIEVqH66+/Xq/n3qSnnnpKjyelnj169FAwB2SB8ho1asiGDRvk5ptvzqkP2jRq1EhhE6/wokWLZN26dQrOixcvVq2A7LPOOkt/h35oASS/++67mm/QoEEx8xugpqaTG6CmRtewlGqA6mcJA1TTz08By22AWgB9ABAFDJnCfuaZZ2S//fbT/88NUPGCApl4IUkAKN5EzpYHPCkLmHv99delevXqcueddyosnnrqqXqtmy5/7LHH5I477pCZM2fqdQBoMAGMeFUrV64sTLXjZb300ktzfoe3FTh9+eWXNf9PP/2kHtYKFSrotfyULl1aiLEFUPGkAs3fffednH322QqS/BDiAHgCkhs3blQPr6sPfwdQP/vsMzn99NO17GuuuUYuuOACLR9ABlhJeHOXLFkiJ510ksydO1d13LJlix6x+ueff+6RH48yafbs2VqHyATUhzWlxQDHNlO2ij+sXcirXgaoXvJJWjy/tktDvo0cdvvmu2Ehz2iLpJJsoHr16snee++t0+NAFPAJcDE1D2RFi0EdPny4wihAR2I6v2nTpvrSCwLqsmXLpFy5cjqVX758ef0hLCAIotwfj+b5558vTZo0yWkdUEmdKOPggw9WTypAeNhhh8mKFSvkwAMP1L+RZ/LkyfrfBQsWqAcUQKQNhBsEAZW2nHPOOTodz4Iw6ks5gGDNmjUVsh2guvo4QMVbCiTjNX7ppZe0fP6Lt5a/kQgX4N+UDajiGabe++yzj0JyZP6lS5eq5zZasil+v45uHlQ//cKe2wDVz0JhBxirX2bb16914c1tgJpE2zCtf/LJJ6vn0cV9MmXNlDpwFwtQv/jiC/VE8nc8hKeddpqu9H/66ac1PpOp9+AiKQeoV111lVSrVk0As7322kuh8r777tPY0khApZl4KgFY4kJdTCswWr9+ffXEEg8KUDKNHw+gEgtL3fDcArfnnXeehiWw48CUKVPUU4oWAGskoOLlpT7Tpk2TIkWKKHjTDjyteD/xEOPNpS4//PDDHoC6du3aPfI7+DZATWKn/l9RBqjJ1zRMJRqg+lnDAND081PAckdTwAA1if0CwGMRD8DmEl5RppwBPhYAAavRVvGzoIjpd0AWgBwyZIiwoIiFUZMmTRJgNOhBBVg7deqkC5BYFb9582a55JJLhJX/LNJiypy8wUR8Kt5WFksxlQ4c8jsWJQGSLI4CHEuWLBkXoAKUgPimTZtk1qxZulgKwGRrrRIlSmhbgFQ0cfUJelAB1DfeeEPjVglXAKwJiyBkgboQr8rirAkTJkT1oEbLH8uc5kH16+gGqH76hT23AaqfhQxQTT8/BSy3AWrI+wDeS8IC3N6pVBfIA/bwkMZK7K+Kp5Wp+3gSQElsbDCx2wALrpznN69yiEElFpbFXYQauPpRf7yb1IWdBQhRKFOmTMziuC+hB8H6AKdM+eNFxbuaW4qWP9r1Bqh5WTT3vxug+ukX9twGqH4WMkA1/fwUsNwGqNYHkqaAA1RCGNIhGaD6WckA1U+/sOc2QPWzkAGq6eengOU2QLU+kDQFiJVlMVi8Htek3TifBRmg5lO4/2UzQPXTL+y5DVD9LGSAavr5KWC5DVCtD2StAgaofqbPAdQNX/kVlKLcxFUTAx3WFPb6fVX8cI35DmsyAPSzjOmX2fr5tS68uW2RVHhtYzVLogKcRnXCCScksUQryhQwBUwBUyBbFAjzB1ym2sAANVMta+0yBUwBU8AUMAVMAVMgTRUwQE1Tw1m1TQFTwBQwBUwBU8AUyFQFDFAz1bLWLlPAFDAFTAFTwBQwBdJUAQPUNDWcVdsUMAVMAVPAFDAFTIFMVcAANVMta+0yBUwBU8AUMAVMAVMgTRUwQE1Tw1m1TQFTwBQwBUwBU8AUyFQFDFAz1bLWLlPAFDAFTAFTwBQwBdJUAQPUNDWcVdsUMAVMAVPAFDAFTIFMVcAANVMta+0yBUwBU8AUMAVMAVMgTRUwQE1Tw1m1TQFTwBQwBUwBU8AUyFQFDFAz1bLWLlPAFDAFTAFTwBQwBdJUAQPUNDWcVdsUMAVMAVPAFDAFTIFMVcAANVMta+0yBUwBU8AUMAVMAVMgTRUwQE1Tw1m1TQFTwBQwBUwBU8AUyFQFDFAz1bLWLlPAFDAFTAFTwBQwBdJUAQPUNDWcVdsUMAVMAVPAFDAFTIFMVcAANVMta+0yBUwBU8AUMAVMAVMgTRUwQE1Tw1m1TQFTwBQwBUwBU8AUyFQFDFAz1bLWLlPAFDAFTAFTwBQwBdJUAQPUNDWcVdsUMAVMAVPAFDAFTIFMVcAANVMta+0yBUwBU8AUMAVMAVMgTRUwQE1Tw1m1E1Ng6tSpcsIJJySWya42BUwBU8AUMAVE5KijjjIdClgBA9QCFtxuVzgK/Otf/5Lu3bsXzs3juOtXX30V6heg1S8OI+ZySdj1K3J7Jb8GWm5TIIMVWHHTW6F+P2eq9AaomWpZa9duChig+nWIsAOW1c/PvgaofvpZ7sxWwAC1cOxrgFo4uttdC1gBA1Q/wQ0AM1s/A1Q/+1ruzFbAALVw7GuAWji6210LWAEDVD/BDVAzWz8DVD/7Wu7MVsAAtXDsa4BaOLoX6l0XLlwoP/zww251qFChQlotIvr2229ly5YtUqVKlbi0NECNS6aYFxmgZrZ+Bqh+9rXcma2AAWrh2NcAtXB0L9S7Nm3aVP7880+pWrVqTj2qVasml19+uVe9nnvuOdmxY4e0adPGqxyXuU6dOjJr1izZa6+99ijv6aefliVLlsiAAQPiupcBalwyGaD6yZS2+hmgpsjwVmxGKGCAWjhmNEAtHN0L9a4AavPmzYX/BhOAOWXKFHnhhRdk+vTpMnToUJk0aZKMHj1aBg8eLMWKFZMuXbpI69atZc2aNQqin376qTRs2FC6desmZ599tmzfvl0mTpwo9evX16K/+eYb6dixo5QtW1Y+/PBD6dy5s5Y9b9486devn7Rv317ee+896dGjh6xcuVKuuOIKGTRokHTt2lUGDhwojRo1kpdeekluu+02GTNmjN7j0UcfVXB99tln5ZdffhG8qQ8//LBceeWVMXU1QPXrcuZBzWz9DFD97Gu5M1sBA9TCsa8BauHoXqh3BUx//vlnqVy5ck49OnToIMccc4yceOKJ0rNnT3nggQdkyJAhctZZZ0mlSpUUKIsWLSo1atSQDRs2SP/+/RUOAcd27drJ9ddfL/Pnz5fffvtN+vbtK8WLF9eyv/zySzn22GMVckuVKiUtWrSQESNGyKGHHqp5Vq9eLaeddpoCLlBbt25dGTlypNajTJky8uOPP2q5lDlhwgQZNmyYbNu2TcMRAOS5c+fK8uXLtT6LFy82QE1RzzJA9RM27PoZoPrZ13JntgIGqIVjXwPUwtG9UO8KoB5wwAFSq1atnHo0aNBADj/8cJk5c6bUq1dPmjVrpp7UXbt2KWTi5Vy0aJF6Nzdu3KjXjB07ViF3/fr1OrU/fvx4+fXXX6VXr1455ZK3evXq8tNPPylsEuvKtfyULl1ayyce9q233lLQZMp+3LhxcvHFF0uRIkX0ut69e8txxx0nbdu2VShetWqVgukrr7yiP5s3b5b99ttPy4qVzIPq1+XCDlhWPz/7GqD66We5M1sBA9TCsa8BauHoXqh3jTXFT6Xef/99qV27tk7j4/UEHg888EDd5L5mzZo6BQ9s4lVdtmyZHHzwwQqNf/zxh3o+owFqkyZNZMGCBep5Pemkk2TdunUaCgCgEgtLuXhvL7zwQhk1apTeKwioV111ld6XH+6zadMm4WQoF4OKRxVvqwPU2bNnK1BHJtuoP//dzgAw/9qRM+z6GaD62ddyZ7YCBqiFY18D1MLRvVDvCqACe4CjS3grd+7cKaeeeqp06tRJIXHy5Mm6Up74UzycxKcSEwqgEkuKFxWQbdmypTRu3Fi+/vpr2bp1q/Tp02c3D2pugLp27Vr1qgK5lMvUP/DpABWQff755xWcCQ0AYPHk4pWNBajRxDUPql+XCztgWf387GuA6qef5c5sBQxQC8e+BqiFo3uh3pXpexYeBRMr+IkFZQHTjBkzdPr+7rvvljlz5siZZ56pIQElSpRQzyeQysIoFj+xWIqtnqZNmyZvv/22gioLq4BXElP8uQEqXk+AeenSpXo98aqA6jvvvCOs4sczyuKtG2+8UeuC1/W1114TvKQGqAXXjQwA/bQOu34GqH72tdyZrYABauHY1wC1cHRPq7syDY+nk+l8ptiZxgccSUy3E//pEgALyEbbGiq3RgO65cuX13zEuJYrV07v9fvvvyuURrtXIiKaBzURtfa8NuyAZfXzs68Bqp9+ljuzFTBALRz7GqAWju521wJWwADVT3ADwMzWzwDVz76WO7MVMEAtHPsaoBaO7nbXAlbAANVPcAPUzNbPANXPvpY7sxUwQC0c+xqgFo7udtcCVsAA1U9wA9TM1s8A1c++ljuzFTBALRz7GqAWju521wJWwADVT3AD1MzWzwDVz76WO7MVMEAtHPsaoBaO7nbXAlbAANVPcAPUzNbPANXPvpY7sxUwQC0c+xqgFo7udtcCVsAA1U9wA1TTz08Bv9zW/0w/PwX8coe9//m1Lry5DVDDaxurWRIVMED1EzPsL2irn9nXTwG/3Nb/TD8/BSx3NAUMUK1fZIUCBqh+ZrYBOLP1u2xQEb8GWm5TIAEFzjmhldxywZicHPZ+SUC8LLrUADWLjJ3NTTVA9bO+DSCZrZ8Bqp99LXdiChigJqZXtl5tgJqtls+ydhug+hncADWz9TNA9bOv5U5MAQPUxPTK1qsNULPV8lnWbgNUP4MboGa2fgaofva13IkpYICamF7ZenWoAXXFihV6NvsRRxyRkH1WrVol3333nZx11llR87377rtSp04dKVIkfeKuOKv+559/lsqVKyekhc/F3377rWzZskWqVKmSUDHff/+95iuIum7cuFH23Xdf7Se5JQPUhEy4x8UGqJmtnwGqn30td2IKGKAmple2Xp0yQF28eLEMHz5cHn300Xxr+9BDD0mpUqXk5ptvjrsMoOqEE06QF198US644IKo+QCaH374QUqUKBF3uYV94fPPPy9z586VIUOGFFhVnn76aVmyZIkMGDBgt3vedtttcs0118ipp54atS7vv/++fiBcccUVKavrN998I1deeaUceOCBUqxYMalevbr07t075v0MUP1MYYCa2foZoPrZ13InpoABamJ6ZevVMQEVIJo9e7Z8+OGHsmnTJhk2bJg0aNBA//+6666TlStXyrXXXiuDBw9Wb9ktt9wi//3vf+XMM88UwKZu3bqycOFC6d+/v7Rq1UratGkjn376qTRs2FCeeOIJ+fHHH6Vdu3byzjvvqDdz1KhRUqFCBQFubrjhBoUO4OPiiy+WTp06yYMPPqh1OPTQQ+Wxxx5TIImWKOu9996TPn36yF133SXA1MSJE+XII48UgPf0009XjxuAumzZMrnxxhuFtv7+++9y/fXXy5dffiktW7aU+++/X+sQlhQE1Ndee026desmf/zxh7bx6quvVhhHI6ARnfv16ycvr6P1gwAAIABJREFUvfRSVN3q1asnZ5xxhjz55JNSo0YNGT9+vOzcuXMPG77yyivy7LPPyi+//CKA/8MPP6xyNG/eXO2wYMECmTNnzh51efXVV+WLL76Qpk2bSufOnWXvvfeWmTNnaj0HDhwYU1L62XPPPSfHHXec9rXcEvbZsWOH3HPPPfLrr7/qPYDigw8+OGo2A1S/nmyAmtn6GaD62ddyJ6aAAWpiemXr1TEBddCgQcKgDjAuXbpUge/jjz9WmAQ0mD4HOh955BFZtGiR/gBF3bt3Vzg96KCDpG/fvvLGG28oGAI5wCJQ2rFjRy1r9erV0rNnTy2baWT+feKJJyqg1qxZUxo1aqReMcpr0aKFekWnT5+ukMM0ftGiRfewG/W99dZb5a233lL4xftHPuAZEJs6daoCKqB93nnnyejRo6V+/foKU8A1/wbAgWLAOizJASp2OeSQQ7QtgBlexE8++UQef/xx2W+//eTuu+9WPZnyvuyyy6LqVrFiRYVwPNO0G43Xrl27hw0BQGyM53b58uX6scG9LrnkEoVZIBJQjawLHzZ8nAD/xx57rOpPmMZpp50m69atk/Lly+8mK2XjbX/qqae0zvQvIDuY/v73v2vfcWn79u0aooGHffLkydq3+LiIFbZhgOrXkw1QM1s/A1Q/+1ruxBQwQE1Mr2y9OldAxWsGEJGOPvpowTN2zjnnKGSQ8HoyBQzUNWvWTD1pl156qZx88skKgD169JA333xTvXRjx47VmMT169er54vr3O8AFPL/5z//0WvwpAGfgCrT9Vu3blWvaOPGjfW+gApTvIcddtgedvvss88UgIEkPIyAMFAFsBLTyv8DqGXLlpWLLrpIvbnATunSpdVDy7Q/deYa/j8syQEqQAo49+rVS6uGN/mOO+6Qww8/XPXCq4mGkyZNUnCLphvwjw7AJR8FeB2POeaYPWyIJxwvKj+bN29WAN61a5d6wfkQwUbR6gI4O0A9//zzVXcSgIreLjSADx/qTp35IGjdurV+2BAe8vLLL+8BqABvMOH1xmvMBwt1PPfcc2OaywDVrycboGa2fgaofva13IkpYICamF7ZenWugAoY4pEjAYN4S5lSBiBITBHzb6b5gTogYcyYMQquABOAOmPGDIVBptMBITypACrlAS5M4/O3Jk2ayLhx4zRuEWAl3XnnneotBGRYeIMn1SVgply5crkCKt5SYBaPKsBJvRyg8rt///vfAtDibQTWHIxT6PHHHy8XXnhhaPqFA9TatWurTYBwl5iyx/OMvlyHdxO7AHTRdKNtaI5+eFvxTuJNjbQh9ncxqNu2bZMyZcrsBqiUHa0ufBA4QMWuACgJ7zsxyQ5QCVUAdrt06aJ1BZJJwDDe9WDCU+r+zu+BYD5q+KCgTLzCLvFxAphHJqA6rMkA0M8ypp+ffgaofvpZ7sQUMEBNTK9svTpXQAU43377bYU4wBFQBAQY/AE6AIG4zXnz5qmXFK8oHj2ghvhUpl2BBRbUAFFAJVPLeELx7uF5xQOHF4zV2EwhU/6sWbOkUqVKGifZvn17jR995plnFGCJMwQcmQKOtsgp6EEF1IAvoPr222/XtjhAJQaV3xNLyxT0KaecorGz1apVU+gC9vBIhiU5QAUoqSO2AKwBwPvuu0/179q1q3pOO3TooN5sAD2abnwcRAIqekTasFatWjEBFduiWbS6EO4RD6Ci7YYNG1R3Fn9VrVpVbrrpJvXM8gERTHiFp0yZkvOrESNGaPuCv8vNVuZB9evJBoCZrZ8Bqp99LXdiChigJqZXtl6dK6DiGWXqmy2ORo4cqfGOAA/Ty0yvHnXUURoT6qb5mdoHNFlYA2AClkwhE6uIx49yiDWdNm2aTrsT6/nTTz9pWSyiwQtITChgyaIXygBg27Ztq3ALXDJQDh06VEEmWgoCKvGol19+eU5dXn/9dZkwYYLGXbJICw8h9wCW2cIJ6CLhrSN+9YADDghNvwBQCZvAi33vvfeqPfA0Eg/KwiJiLz/44AONo8UzjW3wVkfTjWl0B6jECQPxTL/zwRC0IZAZzYPKwiRiXoFkbBFZF7zo8QKqE5hwDPoF5fFBw09uidhYwDaYPv/8c415jZYMUP26sgFqZutngOpnX8udmAIGqInpla1X5wqoTKPiRSxZsqSCqksAJXGoeFHdohRgiClfYlXd74COP//8M8fTSWwpcYzBxGInpvGDK+bZFYC8kVP4LOShLvweAAJIIhOr/oFbl6gX7dh///0V2LhPrO2luCexrSzoibYAK0ydBKAGsGOtWg/WNahbbm2IZsNY13N/wiZIidQlHg2xQ7J3UDBAjUf52NcYoGa2fgaofva13IkpYICamF7ZenWegIoHNIwJsMUjG5nYfiqd9jcNo7aZWCcDVD+rGqBmtn4GqH72tdyJKWCAmphe2Xp1TEBlZTsp6I3MVpGs3emvgAGqnw0NUDNbPwNUP/ta7sQUMEBNTK9svTplJ0llq6DW7nAqYIDqZxcD1MzWzwDVz76WOzEFDFAT0ytbrzZAzVbLZ1m7DVD9DG6Amtn6GaD62ddyJ6aAAWpiemXr1Qao2Wr5LGu3AaqfwQ1QM1s/A1Q/+1ruxBQwQE1Mr2y92gA1Wy2fZe02QPUzuAGq6eengF9u63+mn58CfrnD3v/8Whfe3Aao4bWN1SyJChig+okZ9he01c/s66eAX27rf6afnwKWO5oCBqjWL7JCAQNUPzPbABxDv9Vvi7x4jp+4Icj91eV/He4R1mT9z88ypl9m6+fXuvDmNkANr22sZklUwADVT0wb4AxQ/XqQX27rf6afnwJ+ucPe//xaF97cBqjhtY3VLIkKGKD6iRn2F3Sh1c88qH4dK87chWZfq1+cCvhdZvb10y9TcxugZqplrV27KWCA6tchbAAxD6pfD/LLbf3P9PNTwC932PufX+vCm9sANQW22bVrl7z11ltyyimnyAEHHKB34FhWzqyvVKmSzJ8/X84444yE7kx5derUkeLFiyeUL2wX//LLL7Jw4cKE2+/bDgNUPwXD/oIutPqZB9WvY8WZu9Dsa/WLUwG/y8y+fvplam4D1BRY9r333lOY7NOnj/Tt21fv8Pzzz8vcuXPl9ttvlwYNGsjy5cvzvPNzzz0nO3bskDZt2shBBx0ky5Ytk3LlyuWZL8wXrF+/XkaOHCm9evUq0GoaoPrJbQOIeVD9epBfbut/pp+fAn65w97//FoX3twGqCmwTYcOHdRTOmzYMFm1apUULVo0T0AdPXq03H333VoboLZu3bpSu3Zt2b59u0ycOFFatmwpjRs31n/XqlVLxo0bJ3/729/ktddek27duskff/whd911l1x99dVy7733KtiOHTtWQbhYsWJaLl5cYPfTTz+Vhg0byhNPPCE//vijtGvXTt555x2F6lGjRsnvv/8uHTt2lLJly8qHH34onTt3lunTp8u8efOkX79+0r59e7nttttk586dMnXqVKlYsaI8++yzcuSRR8qTTz4pgwcP1nt26dJFWrduvVt9ZsyYIT179tT6jx8/Xh588EHZunWr9O/fX6644gq933XXXScrV66Ua6+9VsviHm+++aZ89NFH8u2338qAAQOkRYsWUfPHMqcBql9HD/sLutDqZx5Uv44VZ+5Cs6/VL04F/C4z+/rpl6m5DVCTbNlt27bpdi1ff/21At8jjzyi/83NgwqgHX300fLCCy8I+du2bStffvmlwtlvv/2mwHrooYfKZZddJl27dpVWrVpJ9+7dFTIPOeQQBbVff/1VrrzySvnkk08U4AA6ABlvrUv33HOPMMUOXAKlQOjHH38sq1evVmjE41ulShVp1qyZHHvssQI0lypVSmFwxIgRWofrr79er+fepKeeekqGDh2q9ezRo4eCOSALlNeoUUM2bNggN998c0590KZRo0YKm3iFFy1aJOvWrVNwXrx4sWoFZJ911ln6O/RDCyD53Xff1XyDBg2Kmd8ANckd+n/F2QBiHtTU9Kz4SrX+F59Osa4y/TJbP7/WhTe3AWqSbQOIAoZMYT/zzDOy33776f/nBqh4QYFMvJAkABRv4ueff67gSVnA3Ouvvy7Vq1eXO++8U2Hx1FNP1WvddPljjz0md9xxh8ycOVOvA0CDCWDEq1q5cmVhqh0v66WXXprzO7ytwOnLL7+s+X/66Sf1sFaoUEGv5ad06dJCjC2AiicVaP7uu+/k7LPPVpDkhxAHwBOQ3Lhxo3p4XX34O4D62Wefyemnn65lX3PNNXLBBRdo+QAywErCm7tkyRI56aSTNDwCHbds2SL77ruv/Pnnn3vkx6NsgJrkDm2Amrug5kFNTYeLKNUAy09m0y+z9fNrXXhzG6Am2Tb16tWTvffeW6fHgSjgE+Biaj5WDOrw4cMVRgE6EtP5TZs2FV4qQUB1MahM5ZcvX15/CAsIgij3x6N5/vnnS5MmTXJaB1RSJ8o4+OCD1ZMKEB522GGyYsUKOfDAA/Vv5Jk8ebL+d8GCBeoBBRBpA+EGQUAlnvacc87R6XgWhFFfysG7W7NmTYVsB6iuPg5Q8ZYCyXiNX3rpJS2f/+Kt5W8kwgX4N2UDqniGqfc+++yjkByZf+nSpeq5nT17tkJyZKJeYU02gPhZptD0M0D1M1ycuQvNvla/OBXwu8zs66dfpuY2QE2iZZnWP/nkk9Xz6OI+mbJmSh24iwWoX3zxhXoi+TsewtNOO01X+j/99NMan8nUe3CRlAPUq666SqpVqyaA2V577aVQed9992lsaSSg0kw8lQAscaEuphUYrV+/vnpiiQcFKJnGjwdQiYWlbnhugdvzzjtPwxLYcWDKlCnqKUULwDASUPHyUp9p06ZJkSJFdPEX7cDTClziIcabS11++OGHPQB17dq1e+R38B3NpBaD6tfRbQCJoZ8Bql/HijO39b84hYpxmemX2fr5tS68uQ1Qk2gbAI9FPACbS3hF8egBfCwAAlajreJnQRHT74AsADlkyBBhQRELoyZNmiTAaNCDCrB26tRJFyCxKn7z5s1yySWXCCv/WaTFlDl5g4n4VLytLJZiKh045HcsSgIkWRwFOJYsWTIuQAUoAfFNmzbJrFmzdLEUgMnWWiVKlNC2AKlo4uoT9KACqG+88YbGrRKuAFgTFkHIAnUhXpXFWRMmTIjqQY2WP5Y5DVD9OroNcAaofj3IL7f1P9PPTwG/3GHvf36tC29uA9QQ2QbvJWEBbu9UqgbkAXt4SGMl9lfF08rUfTwJoCQ2NpjYbYAFV87zm1c5xKASC8viLkINXP2oP95N6sLOAoQolClTJmZx3JfQg2B9gFOm/PGi4l3NLUXLH+16A9S8LJr738P+gi60+pkH1a9jxZm70Oxr9YtTAb/LzL5++mVqbgPUTLVsitvlAJUQhnRIBqh+VrIBJIZ+Bqh+HSvO3Nb/4hQqxmWmX2br59e68OY2QA2vbUJdM2JlWQwWr8e1sBtjgOpnARvgDFD9epBfbut/pp+fAn65w97//FoX3twGqOG1jdUsiQoYoPqJGfYXdKHVzzyofh0rztyFZl+rX5wK+F1m9vXTL1NzG6BmqmWtXbspYIDq1yH+r70zgbepbPv/ZaZkeIyJFCFSZole8pLhScg8pDKr6EGGUKJSkvSGHjLLnCneQ2WMUEimTJmnzDPJ2P/zu/7POu8+xz7H3vte+5z7rP27P5/zUees617X/buutdd3X/e91s0bCCuoZhlkZs38o35mCphZ255/ZqOz15qAam9s6JmLChBQzcS0/QM60fxjBdUssQK0TrT40r8AFTA7jPE108+r1gRUr0aW42IF1cUc4A3ETEzqR/3MFDCzZv55Wz+z0dlrTUC1Nzb0zEUFoqKipEiRIi72yK6oABWgAlQgUhTAe7nZElYBAmrC6s2zUQEqQAWoABWgAlSACtxFAQIqU4QKUAEqQAWoABWgAlTAKgUIqFaFg85QASpABagAFaACVIAKEFCZA1SAClABKkAFqAAVoAJWKUBAtSocdIYKUAEqQAWoABWgAlSAgMocoAJUgApQASpABagAFbBKAQKqVeGgM1SAClABKkAFqAAVoAIEVOYAFaACVIAKUAEqQAWogFUKEFCtCgedoQJUgApQASpABagAFSCgMgeoABWgAlSAClABKkAFrFKAgGpVOOgMFaACVIAKUAEqQAWoAAGVOUAFqAAVoAJUgApQASpglQIEVKvCQWeoABWgAlSAClABKkAFCKjMASpABagAFaACVIAKUAGrFCCgWhUOOkMFqAAVoAJUgApQASpAQGUOUAEqQAWoABWgAlSAClilAAHVqnDQGSpABagAFaACVIAKUAECKnOAClABKkAFqAAVoAJUwCoFCKhWhYPOUAEqQAWoABWgAlSAChBQmQNUgApQASpABagAFaACVilAQLUqHHSGClABKkAFqAAVoAJUgIDKHIgIBaKioqRIkSIRMVYOkgpQASpABdxVIF++fO52yN7uqgAB9a4S8QAvKPDxxx9Lz549rR3Kvn37xOYPQPpnljq26zfuu86SKVNms0GG0fr8+XP0z0DfhNAvb7ZiUip/3ZC8tP36sN2/kERPAkYE1CQQJLporgAB1UxD2z+g6Z9ZfFt8nsysA1pHvAKda80loEZ8FrgrAAHVXT3Zm6UKEFDNAkMA9LZ+BFSz+NJahIDKLHBbAQKq24qyPysVIKCahYWA6m39CKhm8aU1AZU54L4CBFT3NWWPFipAQDULCgHV2/oRUM3iS2sCKnPAfQUIqO5ryh4tVICAahYUAqq39SOgmsWX1gRU5oD7ChBQ3deUPVqoAAHVLCgEVG/rR0A1iy+tCajMAfcVIKC6ryl7tFABAqpZUAio3taPgGoWX1oTUJkD7itAQHVfU/ZooQIEVLOgEFC9rR8B1Sy+tCagMgfcV4CA6r6m1ve4ZcsWOX36dAw/c+bMmaR2Wjpy5IhcunRJChcuHJDeBNSAZIrzIAKqt/UjoJrFl9YEVOaA+woQUN3X1PoeGzRoILdv35bHH3882tfixYvLCy+8YOT75MmT5caNG9KyZUujfhzjp59+WlasWCEpUqS4o78JEybI9u3bZdCgQQGdi4AakEwEVDOZkqx+BNQwBT6CuuV7UCMo2Ak0VAJqAglt02kAqE2aNBH869sAmPPnz5cZM2bI4sWLZfjw4TJ37lwZP368fPbZZ5IyZUrp0qWLvPLKK3Ls2DEF0Y0bN0qtWrWkR48eUrFiRbl69arMmTNHqlatql0fOnRIOnToIBkzZpR169bJG2+8oX2vX79eBgwYIG3atJHVq1dLr169ZP/+/dKwYUMZMmSIdOvWTT799FOpU6eOzJw5U7p27SoTJ07UcwwbNkzBddKkSfLnn38KqqmDBw+Wxo0bxykzAdUsA1lB9bZ+BFSz+NKaFVTmgPsKEFDd19T6HgGmFy9elEKFCkX72r59e3nkkUfksccek969e8uHH34oQ4cOlfLly0vevHkVKJMnTy6lSpWSU6dOycCBAxUOAY6tW7eWdu3ayaZNm+TatWvSr18/SZUqlfa9Z88eKVCggEJu2rRppWnTpjJq1CjJnTu32hw+fFjKli2rgAuorVSpkowePVr9SJ8+vZw5c0b7RZ+zZs2SESNGyJUrV3Q5AgB57dq1smvXLvVn27ZtBNQwZR8B1UxY2/UjoJrFl9YEVOaA+woQUN3X1PoeAahZsmSRMmXKRPtarVo1efDBB2XZsmVSpUoVadSokVZS//77b4VMVDm3bt2q1c1z587pMVOnTlXIPXnypE7tT58+Xf766y/p06dPdL+wLVmypJw9e1ZhE2tdcSx+7rnnHu0f62GXL1+uoIkp+2nTpslzzz0nyZIl0+PefvttKViwoLRq1Uqh+ODBgwqm33zzjf5cuHBBMmXKpH3F1VhBNUtL2wGL/pnFl4Bqph+tCajMAfcVIKC6r6n1PcY1xQ/H16xZIxUqVNBpfFQ9AY/ZsmWTnj17SunSpXUKHrCJqurOnTslV65cCo03b97Uyqc/QK1fv75s3rxZK69FixaVEydO6FIAACrWwqJfVG9r1qwp48aN03P5AmqzZs30vPjBec6fPy9RUVHRa1BRUUW11QHUVatWKVDHbujX1kbAMosM9TPTj4Bqph+tCajMAfcVIKC6r6n1PQJQAXsAR6ehWnnr1i0pUaKEdOzYUSFx3rx5+qQ81p+iwon1qVgTCkDFWlJUUQGyzZs3l3r16smBAwfk8uXL8u6778aooMYHqMePH9eqKiAX/WLqH/DpACpAdsqUKQrOWBoAgEUlF1VZ5yGp2IDqLwCsoJqlJQHQ2/oRUM3iS2sCKnPAfQUIqO5ran2PmL7Hg0e+DU/wYy0oHmBasmSJTt/37dtXfvrpJ3nqqad0SUDq1Km18glIxYNRePgJD0vhVU8LFy6UH374QUEVD1YBXtEwxR8foKLqCWDesWOHHo/1qgDVlStXCp7iR2UUD2+99tpr6guqrgsWLBBUSQmoCZdqBFQzrW3Xj4BqFl9aE1CZA+4rQEB1X1PP9YhpeFQ6MZ2PKXZM4wMc0TDdjvWfTgPAAmT9vRoqPmEAutmzZ1c7rHHNnDmznuv69esKpf7OFYzQrKAGo9adx9oOWPTPLL4EVDP9aE1AZQ64rwAB1X1N2aOFChBQzYJCAPS2fgRUs/jSmoDKHHBfAQKq+5qyRwsVIKCaBYWA6m39CKhm8aU1AZU54L4CBFT3NWWPFipAQDULCgHV2/oRUM3iS2sCKnPAfQUIqO5ryh4tVICAahYUAqq39SOgmsWX1gRU5oD7ChBQ3deUPVqoAAHVLCgEVG/rR0A1iy+tCajMAfcVIKC6ryl7tFABAqpZUAio3taPgGoWX1oTUJkD7isQNkDFDQ0tX758MbzGfunYmQivFPLXjhw5oi+Hx7s1Q214pybe2xnXOULtl3ZJVwECqlnsCKje1o+AahZfWhNQmQPuKxA2QMWe7Xh/Zrdu3WJ4PWHCBClWrJjuWOTb8FL2FStWyKRJk6JfwB7qcNu1ayfVq1ePsVNSqH3RzhsKEFDN4khA9bZ+BFSz+NKagMoccF+BsAIqdiTatWuXej106FDdvrJ///5SqVIl3Snoxo0bumMRdibCdpr4t27dugqp2PoS1dTBgwdL48aN/Y4cW2BiR6F169bpC+NHjBgh1apVEwdQ0VfXrl1lzpw58vDDD8snn3wiTz75pPz888/SunVrfeF8p06dpHPnzrJ79261w85H2Lrzgw8+kJQpU7qvOHtMFAUIqGayE1C9rR8B1Sy+tCagMgfcVyCsgAoo+Pbbb2Xz5s0Kmpjex97tAEdsjbl06VKFygoVKujORGfOnNH93lu2bClr165VuB04cKDa+Wuo0uIc2Kcd0/rYA37Dhg3RgIodiAYNGiRff/21fP/99zJ9+nTd5x3V2jfffFOeeOIJeeaZZ9QGW2liS8+qVatK27ZtdT/6l156yX3F2WOiKEBANZOdgOpt/cZ911kyZcpsNsgwWp8/f47+GeibEPrlzVZMSuWvG5KX/HwJSTbPG4UVUPfv3y/Dhg1TEXPkyCEbN26U3r17RwNqyZIldT93tGTJkmlFFfuuf/PNN/pz4cIF3UYT+7XHBaiosgJU0fLnzy8//vij9OvXT6f4UZEFfP7666+yfPly2bt3r+4z/+ijj8qJEyfUBr8DHOfMmVNhGdt0ApwzZMig/8/mDQUIqGZx5A2E+pkpYGbN/KN+ZgqYWduef2ajs9c6rICKKXQAKVqePHl0ar1Pnz7RgOq7TtQXULdv366VzytXrig8xgeoly9flr59+0afY8GCBTJ8+HAF1DRp0uhUPqbwAZzjxo0TrIFt0KBB9NID7PuO5QF4mMsBXXQGiK1Zs6a9kaNnQSlAQA1KrjsOtv0Dmv4xvmYKmFkz/6ifmQK09qdAWAEVVdAffvhBtm7dKo0aNVIo9J3ijw2oV69e1Wn4YADVOcdvv/0mDRs2lJ07d0r79u0VULHONWvWrPLOO+/olD58Wb9+vdx///26dvXBBx+UIkWK6HH//Oc/FV6LFy+uwJsrVy559dVXmTUeUYCAahZI3oD96Lemf8Ci4otw5sz2TqHvy9nijjeuBDy4BDiQ+WcmMvXztn5mo7PXOmyAioeeRo4cKTdv3hRcHBMnTtQ1nb6AWqNGDalXr56qg3WhqJY2adIkKEDFebDW9NixYzJ69Gh9oAoPO6FvTNu/8MILkjdvXn1ICuthZ82aJWfPntVK7vXr19Wf999/X+bNm6cPVKGlTZtWoRWvqmLzhgIEVLM48gbnR7/R+UQu7DcT1hLrffX2ElANYsHrw0A8EWWE2K+kNOvRXWvb/XN3tPb0FjZAdYaIdaQpUqRQ+IyvAWQBjIDN2G3Lli3y+++/3/F7rB+9deuWVjoxne/PFm8DwFKDf/zjH/pmADyZj3WmOBfO6WuD/z906JA89NBDkjx5cnuiRE+MFSCgmklo+wd0ovhHQDVLqiCsEyW+9C8IBcwOZXzN9POqddgB1Q3hDh48qBXS2A0PROHBKmedqxvnYh/eVICAahZX3kD86EdANUuqIKyZf0GI5edQ6udt/cxGZ691kgDUuOTDmlW0dOnS2aswPbNCAQKqWRh4gyOgmmWQmTXzj/qZKWBmbXv+mY3OXuskDaj2ykrPbFOAgGoWEds/oBPFP1ZQzZIqCOtEiS/9C0IBs0MZXzP9vGpNQPVqZDmuGAoQUM0SgjcQVlDNMsjMmvlH/cwUMLO2Pf/MRmevNQHV3tjQMxcVIKCaiWn7B3Si+McKqllSBWGdKPGlf0EoYHYo42umn1etCahejSzHxQqqiznAGwgrqC6mU9BdMf+CliyGAfXztn5mo7PXmoAahthg5ytsrVqsWLHod6niLQQXL17Ud7Ju2rRJypUrF9SZ0R/eFZsqVaqg7Gw7GK/6wmvDgh2/6ThYQTUW4bHMAAAgAElEQVRTkDc4AqpZBplZM/+on5kCZta255/Z6Oy1JqCGITarV69WmHz33XelX79+eoYpU6bI2rVrdUeratWqRW+1Gt/pJ0+erK/RatmypeTIkUN3ybJ5N5pApDx58qRuqICNEhKyEVDN1Lb9AzpR/OMUv1lSBWGdKPGlf0EoYHYo42umn1etCahhiCy2WkWldMSIEYJ3uOKl/3cD1PHjx+sWq2iA2kqVKkmFChUEr9KaM2eONG/eXHfdwn+XKVNGpk2bJvfdd58sWLBAevTooZsOYEvXF198Ud577z0F26lTpyoIY3MCNFRxAbsbN26UWrVqyZdffilnzpyR1q1b685ZgOpx48bpJgYdOnSQjBkzyrp16+SNN96QxYsX6zaxAwYMkDZt2uiuW9gkISoqSreOnTRpku7WNWbMGMHuXjhnly5ddKcuX3+WLFmi762F/9jW9qOPPpLLly/LwIEDdatanK9t27ayf/9+efnll7UvnGPp0qXyyy+/yJEjR2TQoEHStGlTv/ZxhZOAapbovIGwgmqWQWbWzD/qZ6aAmbXt+Wc2OnutCagux+bKlSu6ZduBAwcU+D7//HP9Nz5ABaDlz59fZsyYIbBv1aqV7NmzR+Hs2rVrCqy5c+eWunXrSrdu3XTL2J49eypkPvDAAwpq2C0L27z++uuvCnAAOgAyqrVO69+/v+6mBbgElAJCN2zYIIcPH1ZoRMW3cOHC0qhRIylQoIAAmrHtK2Bw1KhR6gO2kcXxODfa2LFjZfjw4epnr169FMwBsoDyUqVKyalTp6RTp07R/kCbOnXqKGyiKrx161Y5ceKEgvO2bdtUK0B2+fLl9XfQD1oAkrExA+yGDBkSpz0B1eWE/k93tn9AJ4p/rKCGJ9n89Joo8Q1idPQvCLEYXzOxIsiagOpysAGiAENMYX/11VeSKVMm/f/4ABVVUEAmqpBoAFBUE7G9K8ATfQHmvv32WylZsqS89dZbCoslSpTQY53p8i+++EK6d+8uy5Yt0+MAoL4NwIiqaqFChQRT7aiy1q5dO/p3qLYCTmfPnq32Z8+e1Qprzpw59Vj8YGtYrLEFoKKSCmg+evSoVKxYUUESP1jiAPAESJ47d04rvI4/+DsA9bfffpMnn3xS+27RooXUqFFD+wcgA1jRUM3dvn27FC1aVJdHQMdLly5JhgwZ5Pbt23fYo6JMQHU5oQmocQtKQA1PshFgXNeVAG0mqe36mY3OXmsCqsuxqVKliu5shelxQBTgE8CFqfm41qCOHDlSYRRAh4bp/AYNGgguCl9AddagYio/e/bs+oNlAb4givOjolm9enWpX79+9OgAlfAJfeTKlUsrqQDCPHnyyN69eyVbtmz6N9jMmzdP/928ebNWQAGIGAOWG/gCKtbTVq5cWafj8UAY/EU/qO6WLl1aIdsBVMcfB1BRLQUko2o8c+ZM7R//olqLv6FhuQD+G30DVFEZht/33nuvQnJs+x07dmjldtWqVQrJsRv8srXZ/gFI//xkDgE1wS4n5p+Z1NTP2/qZjc5eawKqi7HBtP4TTzyhlUdn3SemrDGlDriLC1B3796tlUj8HRXCsmXL6pP+EyZM0PWZmHr3fUjKAdRmzZpJ8eLFBWCWIkUKhcr3339f15bGBlQME5VKACzWhTprWgGjVatW1Uos1oMCKDGNHwigYi0sfEPlFnD77LPP6rIEvHFg/vz5WimFFgDD2ICKKi/8WbhwoSRLlkwf/sI4UGkFXKJCjGoufDl9+vQdgHr8+PE77B349hdSrkE1S3Te4AioZhlkZs38o35mCphZ255/ZqOz15qA6mJsAHh4iAfA5jRURVHRA/DhASDAqr+n+PFAEabfAbIAyKFDhwoeKMKDUXPnzhXAqG8FFcDasWNHfQAJT8VfuHBBnn/+ecGT/3hIC1PmsPVtWJ+KaiselsJUOuAQv8NDSQBJPBwFcEyTJk1AgAqgBIifP39eVqxYoQ9LATCzZMkiqVOn1rEAUqGJ449vBRWAumjRIl23iuUKAGssi8CSBfiC9ap4OGvWrFl+K6j+7OMKJwHVLNFt/4BOFP9YQTVLqiCsEyW+9C8IBcwOZXzN9POqNQHVosiieollAQA8pwHyAHuokMbV8H5VVFoxdR9IA1Bibaxvw9sG8MCVU/m9Wz9Yg4q1sHi4C0sNHP/gP6qb8AVvFsAShfTp08fZHc6LpQe+/gBOMeWPKiqqq/E1f/b+jieg3i2i8f+dNxA/+hBQzZIqCGvmXxBi+TmU+nlbP7PR2WtNQLU3NlZ75gAqljAkhUZANYsSb3AEVLMMMrNm/lE/MwXMrG3PP7PR2WtNQLU3NlZ7hrWyeBgs0IprYg+GgGoWAds/oBPFP1ZQzZIqCOtEiS/9C0IBs0MZXzP9vGpNQPVqZDmuGAoQUM0SgjcQVlDNMsjMmvlH/cwUMLO2Pf/MRmevNQHV3tjQMxcVIKCaiWn7B3Si+McKqllSBWGdKPGlf0EoYHYo42umn1etCahejSzHxQqqiznAGwgrqC6mU9BdMf+CliyGAfXztn5mo7PXmoBqb2zomYsKsIJqJiZvcARUswwys2b+UT8zBcysbc8/s9HZa01AtTc29MxFBQioZmLa/gGdKP5xit8sqYKwTpT40r8gFDA7lPE108+r1gRUr0aW44qhAAHVLCF4A2EF1SyDzKyZf9TPTAEza9vzz2x09loTUO2NDT1zUQECqpmYtn9AJ4p/rKCaJVUQ1okSX/oXhAJmhzK+Zvp51ZqA6tXIclysoLqYA7yB+BFzTf+AFcYucZkzZw74+IQ+cF/OFrq1sK2N+WcWGernbf3MRmevNQHV3tjQMxcVYAXVTEze4KifmQJm1sw/6memgJm17flnNjp7rQmo9saGnrmoQFRUlBQpUsTFHtkVFaACVIAKRIoCNs8weDUGBFSvRpbjogJUgApQASpABahAElWAgJpEA0e3qQAVoAJUgApQASrgVQUIqF6NLMdFBagAFaACVIAKUIEkqgABNYkGjm5TASpABagAFaACVMCrChBQvRpZjosKUAEqQAWoABWgAklUAQJqEg0c3aYCVIAKUAEqQAWogFcVIKB6NbIcFxWgAlSAClABKkAFkqgCBNQkGji6TQWoABWgAlSAClABrypAQPVqZDkuKkAFqAAVoAJUgAokUQUIqEk0cHSbClABKkAFqAAVoAJeVYCA6tXIclxUgApQASpABagAFUiiChBQk2jg6DYVoAJUgApQASpABbyqAAHVq5HluKgAFaACVIAKUAEqkEQVIKAm0cDRbSpABagAFaACVIAKeFUBAqpXI8txUQEqQAWoABWgAlQgiSpAQE2igaPbVIAKUAEqQAWoABXwqgIEVK9GluOiAlSAClABKkAFqEASVYCAmkQDR7epABWgAlSAClABKuBVBQioXo0sx0UFqAAVoAJUgApQgSSqAAE1iQaOblMBKkAFqAAVoAJUwKsKEFC9GlmOiwpQASpABagAFaACSVQBAmoSDRzdDk6BqKgoKVKkSHBGPJoKUAEqQAWogIjky5ePOiSwAgTUBBacp0scBT7++GPp2bNn4pw8gLPu27fP6g9A+hdAEOM5xHb9Fi3tLJkzZzYbZBitz50750n/Hi/yuqRNmzWMyv3/rm3PP/oX9hRIkicgoCbJsNHpYBUgoAarWMzjeQPxtn7/HpPMbIC0DkmBVi+eIqASoEPKnUgwIqBGQpQ5RiGgmiUBAdXb+hFQzeIbqjUBlRXeUHMnEuwIqJEQZY6RgGqYAwRUMwFt14+AahbfUK0JqATUUHMnEuwIqJEQZY6RgGqYA7YDFv0zCzAB1Uy/UK0JqATUUHMnEuwIqJEQZY6RgGqYAwRAMwFt14+AahbfUK0JqATUUHMnEuwIqJEQZY6RgGqYA7YDFv0zCzAB1Uy/UK0JqATUUHMnEuwIqJEQZY6RgGqYAwRAMwFt14+AahbfUK0JqATUUHMnEuwIqJEQZY6RgGqYA7YDFv0zCzAB1Uy/UK0JqATUUHMnEuwIqJEQ5Vhj3LJli5w+fTrGb3PmzJmkdlo6cuSIXLp0SQoXLhxQBPmaqYBkivMgAqC39SOgmsU3VGsCKgE11NyJBDsCaiREOdYYGzRoILdv35bHH388+i/FixeXF154wUiNyZMny40bN6Rly5ZG/TjGTz/9tKxYsUJSpEhxR38TJkyQ7du3y6BBgwI6FwE1IJkIqGYyJVn9CKhhCvxduiWgElATJ/OSxlkJqEkjTq56CUBt0qSJ4F/fBsCcP3++zJgxQxYvXizDhw+XuXPnyvjx4+Wzzz6TlClTSpcuXeSVV16RY8eOKYhu3LhRatWqJT169JCKFSvK1atXZc6cOVK1alXt+tChQ9KhQwfJmDGjrFu3Tt544w3te/369TJgwABp06aNrF69Wnr16iX79++Xhg0bypAhQ6Rbt27y6aefSp06dWTmzJnStWtXmThxop5j2LBhCq6TJk2SP//8U1BNHTx4sDRu3DhOnQioZinECqq39SOgmsU3VGsCKgE11NyJBDsCaiRE2U8F9eLFi1KoUKHov7Rv314eeeQReeyxx6R3797y4YcfytChQ6V8+fKSN29eBcrkyZNLqVKl5NSpUzJw4ECFQ4Bj69atpV27drJp0ya5du2a9OvXT1KlSqV979mzRwoUKKCQmzZtWmnatKmMGjVKcufOrTaHDx+WsmXLKuACaitVqiSjR49WP9KnTy9nzpzRftHnrFmzZMSIEXLlyhVdjgBAXrt2rezatUv92bZtGwE1TPlMQDUT1nb9CKhm8Q3VmoBKQA01dyLBjoAaCVH2A6hZsmSRMmXKRP+lWrVq8uCDD8qyZcukSpUq0qhRI62k/v333wqZqHJu3bpVq5vnzp3TY6ZOnaqQe/LkSZ3anz59uvz111/Sp0+f6H5hW7JkSTl79qzCJta64lj83HPPPdo/1sMuX75cQRNT9tOmTZPnnntOkiVLpse9/fbbUrBgQWnVqpVC8cGDBxVMv/nmG/25cOGCZMqUSfuKq7GCapbotgMW/TOLLwHVTL9QrQmoBNRQcycS7AiokRBlP4Dqb4ofh61Zs0YqVKig0/ioegIes2XLJj179pTSpUvrFDxgE1XVnTt3Sq5cuRQab968qZVPf4Bav3592bx5s1ZeixYtKidOnNClAABUrIVFv6je1qxZU8aNG6fn8gXUZs2a6Xnxg/OcP39eoqKiotegoqKKaqsDqKtWrVKgjt3Qr62NgGUWGepnph8B1Uy/UK0JqATUUHMnEuwIqJEQZT+ACtgDODoN1cpbt25JiRIlpGPHjgqJ8+bN0yflsf4UFU6sT8WaUAAq1pKiigqQbd68udSrV08OHDggly9flnfffTdGBTU+QD1+/LhWVQG56BdT/4BPB1ABslOmTFFwxtIAACwquajKOg9JxQZUfyFlBdUs0QmA3taPgGoW31CtCagE1FBzJxLsCKiREOVYY8T0PR488m14gh9rQfEA05IlS3T6vm/fvvLTTz/JU089JVgSkDp1aq18AlLxYBQefsLDUnjV08KFC+WHH35QUMWDVYBXNEzxxweoqHriYa0dO3bo8VivClBduXKl4Cl+VEbx8NZrr72mvqDqumDBAkGVlICacMlLQDXT2nb9CKhm8Q3VmoBKQA01dyLBjoAaCVE2HCOm4VHpxHQ+ptgxjQ9wRMN0O9Z/Og0AC5D192qo+NwA6GbPnl3tsMY1c+bMeq7r168rlPo7VzDDYgU1GLXuPNZ2wKJ/ZvEloJrpF6o1AZWAGmruRIIdATUSoswxcicpwxwgAJoJaLt+BFSz+IZqTUAloIaaO5FgR0CNhChzjARUwxywHbDon1mACahm+oVqTUAloIaaO5FgR0CNhChzjARUwxwgAJoJaLt+BFSz+IZqTUAloIaaO5FgR0CNhChzjARUwxywHbDon1mACahm+oVqTUAloIaaO5FgR0CNhChzjARUwxwgAJoJaLt+BFSz+IZqTUAloIaaO5FgR0CNhChzjARUwxywHbDon1mACahm+oVqTUAloIaaO5FgFzGAin3asSMSXmXEFnkK8DVTZjEnAHpbPwKqWXxDtSagElBDzZ1IsPM0oAJKR44cKcOGDZMJEyZIsWLFdKcktshTgIBqFnMCqrf1I6CaxTdUawIqATXU3IkEu7ACKvZcb926dfSuQNimEttaYsvKd955R/dz/+CDD6RatWp3/A57s7/11lvy9ddfaxywQ9Hw4cNl8ODBuiUntsO8//77ZdKkSfLwww/LmDFjdEvOlClTSpcuXXQLTgDpli1bZODAgfpy+UqVKunuRN27d5exY8eq3ZdffinlypXTnY/wL/opVaqUTJ8+XTJkyBAJORARYySgmoWZgOpt/QioZvEN1ZqASkANNXciwS6sgNq/f385fPiw9O7dW/dnx5aY2MM9f/78Cq379+/XPd9Xr159x+8AiE2aNJHNmzdrHAoVKiTff/+97hOPBsAEsF67dk169eqlsLt+/XpJnjy5AuapU6fkxx9/lH79+smiRYt0q8y6desqdAJ8v/rqK90yE1t0Ll26VHLkyKF7ynfq1Em33nz77bcF23+yeUMBAqpZHAmo3tZv0dLOunubrc3ZXc5r/j1e5HVJmzZr2IfF69dMYtv1MxudvdZhBVSAIvZ0B1zu2rVLsAf8hx9+KDNmzFBARNu4caMcPXpUK6W+v8N2mc2aNfMLqG3atFHYhF3FihV1v3f8AHS3bt0qQ4YM0e0yf//9d4VXACgqqrDBfu4FCxbUyi4awBRLAR577DHZsGGD5M6dW4Ea23o6MGxv+OhZoAoQUANVyv9xtn9A0z/G10wBM2vmH/UzU4DW/hQIG6D+/fffkjFjRtm7d68+nLRz506pX7++Qt/27dt1XSga9nifM2eO7NixI8bvzp49K02bNo0GVFQ+MV0P+zfffFMqV66sFVhM4+PDAedANbZ06dLSsGHDOAF12rRp2i9gFQ39Am4ff/xx9RFVBFR7s2bNqtVUNm8oQEA1iyNvwNTPTAEza+Yf9TNTwMza9vwzG5291mEDVAz5pZdekqpVq+q/H330kUIjKpe1a9fWaiWm/zGdPnv2bKlTp06M32E6v0iRIlolPXjwoAIkgBSAWqZMGYXIL774QqHy2Wef1fWny5cvl/nz52tfANzdu3dL165dZdWqVdEVVPiASivWnv7888869Y9lBKikElDtTVRTzwioZgra/gEdtH8r+psJEqS17VPU+/K0kHz58gU5qoQ7POj4Jpxreib6ZyY49TPTz6vWYQXUdevWSdu2bRUWr1+/LsuWLdOpdKwBnTJlimqKSiqqmf5+h/WqWA6AafcjR47I2rVrFVBRbcXDUOfPn5cVK1bow1IlS5aULFmyCJYGXL16VSH19ddf1wehMGUP+MR5KlSooJCMpQXoE2tdGzduHANQsW4VFVRO8Xsn7QmoZrH03A3k+84i6z43E8VD1vua7yWgGsTTc9eHgRahmFK/UFTzvk1YAdWRDxXQBx54QKHSaZcuXZJ06dLd9XcXL16M8TR9rVq1FGbxoBXeaZoiRQrt8vbt27pcAGtHb968qU/tp0+fXv8bfwO4+rY//vhDMmXKJPfcc4/3o8wR8kX9hjnguRsIATVGRhBQzS4Qz10fZnIEbU39gpYsIgwSBFDdVNIBVLwuio0KBKoAK6iBKuX/OM/dQAioBFSzSyKmfvv2sQJtoKfnPl8MtKDp/ymQ5ADVX+WVAaUCd1OAgHo3heL/u+duIARUAqrZJUFApX4uKsCu/CmQ5ACVYaQCoShAQA1Ftf+zIaCa6We7Naf4zSLkuevDTI6gralf0JJFhAEBNSLCzEESUM1ywHM3EFZQWUE1uyRYQaV+LirArlhBZQ5ErAIEVLPQE1DN9LPdmhVUswh57vowkyNoa+oXtGQRYcAKakSEmYMkoJrlgOduIKygsoJqdkmwgkr9XFSAXbGCmkA5gF20sGkAdrnCu1nRjh07JnhlVt68eWXTpk1Srly5oLxBf3hzQapUqYKys+3gP//8U3cEC3b8puMgoJopSEA10892a1ZQzSLkuevDTI6gralf0JJFhAErqGEIM3aqAkxityu89B8NGxNgowFs01qtWjXZtWvXXc88efJkuXHjhrRs2TLGRgJ3NbT4gJMnT8ro0aOlT58+CeolAdVMbs/dQFhBZQXV7JJgBZX6uagAu2IFNYFyoH379lopHTFihG7Tmjx58rsC6vjx46Vv377qIaC2UqVKuusVdsWaM2eONG/eXOrVq6f/ja1ep02bJvfdd58sWLBAevTooRsSvPPOO/Liiy/Ke++9p2A7depUBWFngwRUcQG72EUL75PFdq9nzpzRnbVWrlypUD1u3Djd9atDhw6SMWNGwW5g2NFr8eLFsn79ehkwYIC0adNGt5C9deuWREVF6U5ekyZN0l27xowZo9vO4pxdunTRLWZ9/VmyZInu7AX/sYsXtsC9fPmyDBw4UBo2bKjnw+5j2Nb25Zdf1r5wjqVLl8ovv/yiu38NGjRImjZt6tc+rhATUM2Sn4Bqpp/t1qygmkXIc9eHmRxBW1O/oCWLCANWUF0O85UrV/SFzQcOHFDg+/zzz/Xf+CqoADTsjIVtXWHfqlUr2bNnj8LZtWvXFFix3Su2au3WrZu89NJL0rNnT4VM7NAF0MPOWdiy9ddff1WAA9ABkFGtdVr//v0FU+yAS0ApIHTDhg1y+PBhhUZUfAsXLiyNGjWSAgUKCKA5bdq0CoOjRo1SH9q1a6fH49xoY8eOleHDh6ufvXr1UjAHyALKS5UqJadOnZJOnTpF+wNtsA0tYDNHjhyydetWOXHihILztm3bVCtAdvny5fV30A9aAJJ//PFHtRsyZEic9gRUlxP6P9157gbCCiorqC5eKp67PlzUJpCuqF8gKkXeMQRUl2MOEAUYYgr7q6++0u1U8f/xASqqoIBMVCHRAKCoJv7+++8KnugLMPftt99KyZIldatXwGKJEiX0WGe6/IsvvpDu3bvLsmXL9DgAqG8DMKKqWqhQIcFUO6qstWvXjv4dqq2A09mzZ6v92bNntcKaM2dOPRY/2BoWa2wBqKikApqPHj0qFStWVJDED5Y4ADwBkufOndMKr+MP/g5A/e233+TJJ5/Uvlu0aCE1atTQ/gHIAFY0VHO3b98uRYsW1eUR0BEbNWTIkEG3r41tj4oyAdXlhCaghkdQy3plBdUsIAQs6memAK39KUBAdTkvqlSpIunSpdPpcUAU4BPAhan5uNagjhw5UmEUQIeG6fwGDRoIPvR8AXXnzp2SOXNmncrPnj27/mBZgC+I4vyoaFavXl3q168fPTpAJXxCH7ly5dJKKoAwT548snfvXsmWLZv+DTbz5s3Tfzdv3qwVUAAixoDlBr6AivW0lStX1ul4PBAGf9EPqrulS5dWyHYA1fHHAVRUSwHJqBrPnDlT+8e/qNbib2hYLoD/Rt8AVVSG4fe9996rkBzbfseOHVq5XbVqlUJy7Aa/bG28wZlFJmj9WEGNITgBNYHzz+x0QVsHfX0EfQYzA/pnpp9XrQmoLkYW0/pPPPGEVh6ddZ+YssaUOuAuLkDdvXu3ViLxd1QIy5Ytq0/6T5gwQddnYuodFdTYgNqsWTMpXry4AMxSpEihUPn+++/r2tLYgIpholIJgMW6UGdNK2C0atWqWonFelAAJabxAwFUrIWFb6jcwrdnn31WlyXgjQPz58/XSim0ABjGBlRUeeHPwoULJVmyZAreGAcqrYBLVIhRzYUvp0+fvgNQjx8/foe9A9/+Qso1qGaJ7rkbCAGVgGp2ScTUb98+Xdpla/Pc9ZvAQtuuXwLLkWCnI6C6KDUADw/xANichqooKnoAPjwABFj19xQ/HijC9DtAFgA5dOhQwQNFeDBq7ty5Ahj1BVQAa8eOHfUBJDwVf+HCBXn++ecFT/7jIS1MmcPWt2F9KqqteFgKU+mAQ/wODyUBJPFwFMAxTZo0AQEqgBIgfv78eVmxYoU+LAXAxKu1UqdOrWMBpEITxx/fCioAddGiRbpuFcsVANZYFoElC/AFH/h4OGvWrFl+K6j+7OMKJwHVLNFt/4AO2j8CKgHV7JIgoFI/FxVgV/4UIKBalBeoXmJZgPPuVLgGyAPsoUIaV8P7VVFpxdR9IA1AibWxvg1vG8ADV07l9279YA0q1sLi4S4sNXD8g/+obsIXvFkASxTSp08fZ3c4L5Ye+PoDOMWUP6qoqK7G1/zZ+zuegHq3iMb/96AB0Ox0QVsH7R8BlYAadJbFbRB0/rl47kC6on+BqJR042s2OnutCaj2xsZqzxxAxRKGpNAIqGZR8twNjoBKQDW7JGLqxyl+IzU99/lipAaNHQUIqMyFkBTAWlk8DBZoxTWkk7hoREA1E9NzNxACKgHV7JIgoFI/FxVgV/4UIKAyLyJCAQKqWZgJqGb62W7Np/jNIuS568NMjqCtqV/QkkWEAQE1IsLMQRJQzXLAczcQVlBZQTW7JFhBpX4uKsCuWEFlDkSsAgRUs9ATUM30s92aFVSzCHnu+jCTI2hr6he0ZBFhwApqRISZgySgmuWA524grKCygmp2SbCCSv1cVIBdsYLKHIhYBQioZqEnoJrpZ7s1K6hmEfLc9WEmR9DW1C9oySLCgBXUiAgzB0lANcsBz91AWEFlBdXskmAFlfq5qAC7YgWVORCxChBQzUJPQDXTz3ZrVlDNIuS568NMjqCtqV/QkkWEASuoERFmDpKAapYDnruBrOhvJkiQ1tglLnPmzEFaJdzh+/K04F7yBnJ77vow0CIUU+oXimretyGgej/GHKGIEFDN0oA3EOpnpoCZNfOP+pkpYGZte/6Zjc5eawKqvbGhZy4qEBUVJUWKFHGxR3ZFBagAFaACkaJAvnz5ImWo1oyTgGpNKOhIOBWYOgB9rxIAABQ4SURBVHWqHD58OJynYN9UgApQASrgQQXy5MkjzZo18+DI7B4SAdXu+NA7lxSwfYqf/pkFmvpRPzMFzKyZf9TPTAFa+1OAgMq8iAgFeAMxCzP1o35mCphZM/+on5kCZta255/Z6Oy1JqDaGxt65qICtn/A0D+zYFM/6memgJk184/6mSlAa1ZQmQMRqwBvIGahp37Uz0wBM2vmH/UzU8DM2vb8MxudvdasoNobG3rmogKrVq2Sp59+2sUe3e2K/pnpSf2on5kCZtbMP+pnpgCtWUFlDlABKkAFqAAVoAJUgApYrwArqNaHiA5SASpABagAFaACVCCyFCCgRla8Odp4FLh48aJkyJAhxhFXrlyRdOnSSfLkyRNMuwsXLkjGjBkT7HzBnujMmTOqU6pUqYI1TbDj//jjD8mVK1eCnS+YE0G/LFmyBGMS9mMvXbok9913X9jPE+oJbNQs9lhszzmbr9nE/sy7fPmy3HvvvZIsWbLosCbGZ3+o14dX7QioXo0sxxWwAnv27JEVK1bIkCFDZNu2bWp3+vRpfTFzypQp5eDBg9K9e3d55ZVXAu4zlAPXrVsnH374oWTKlElOnjwp/fv3lzJlyoTSVVhsoEPz5s0FL62Gf506dZK6deuG5VwmnWLXsOeff15u374d44Zj0qcbtsuWLZNXX31VSpYsKbj5tWnTRmrXru1G1yH3sWHDBmnVqpXkzZtX83zs2LFSunTpkPtz29BGzfyN0dacs/2aTezPPHyObdmyRerVqye4D2TPnj1RPvvdvm680h8B1SuR5DhCVmDgwIGydetW2bRpUzSg4neoKg0YMECOHz8u999/v0LFPffcE/J57mbYsmVLKVu2rEIMnho9cuSIDBs27G5mCfZ3AHrNmjWlcePGgooWPtgrV66cYOcP5EQHDhyQPn36CHYOsw1QoRV8q1q1qqxcuVLatm0ru3btCmRYYTumWrVq0q1bN8G/s2fPli+//FIWLVoUtvMF27GNmsUeg805Z/s1m9ifecj51atXy2effSYnTpxQQE2Mz/5gr4tIOZ6AGimR5jjjVQCw1bRp02hARXULINGkSRP5+++/dYp/7969Es79mFEtQkWyTp06Mm/ePAWFcuXKWRO5p556Squn3333nTzzzDMyaNAgefTRR63x79q1a/LPf/5TJk6cqH7aBqj4woMvOClSpJCuXbvK1atXZcSIEYmqH3Ras2aN6rVx40apUaOG3qhtaTZq5quN7Tln+zVry2cepvYdQE2Mz35brjfb/CCg2hYR+hMWBf766y+/laHq1atLmjRptBroC6iNGjUS/DRo0ED9yZEjh6xdu1YeeughY/+WLl2q1VjfhmnVUaNGycyZM7VCOWXKFGnXrp28+eabxucLtoO4/CtRooRUqlRJPvnkE63snj9/XsaMGRNs98bHx+Ufqs7FixcXVGVww0ksQI3LP6yJxU3w9ddfl927d8v8+fN1aj0xG9YlooqLGYJ9+/ZpfA8fPpyYLt1xbts083XwX//6lxU5F1fA8LllwzUbl3/9+vWz4jPPF1DD+dlv1YWVBJwhoCaBINFFcwUAU+3bt7+jI0AhHkiKDajvvfeePgjUuXNnuXXrlmTOnFmBzI2HpVA9O3r0aAxf8DusR5w7d66UL19eUFnAGk9nTay5AoH3EJd/WKs4ePBgneZHNRl+Jka1zZ9/WBaB6WBnze769ev1v/F+ytSpUwc+eBeOjEs/QCB8fOmll6Rnz56SNm1aF85m1kXFihV1erNUqVLyyy+/CPIe4GxLO3TokHWaOdrgS2b69OmtyLm44vXYY49Zcc3GB9A2fOb5Amo4P/ttua6Sih8E1KQSKfoZVgViAypu0sOHD9eqK6qaeIDqp59+CqsPmJ4GvGBZASqomOb/+uuvw3rOYDpv3bq1VpLxINfkyZPl+++/l0mTJgXTRdiOxTIMPOTgtIIFC8rOnTsF//o+mRs2BwLoGBV6QDMA1paGCn3WrFmlR48euhYVwPX+++/b4p7OatimmSNOUsg5m69Z6GjLZ54voCbGZ781F5xljhBQLQsI3UkcBWIDKtYH4sNz+/btulZw8eLF8uSTT4bVuR9++EFatGih6xTx9oDRo0drldKWhing1157Tau6uXPnln//+9+CaX8bW2JO8cdXLcJTw07DAxmJUYH29c+phON3eHsEvoT94x//sCak+EJkm2ZxiWNjztl+zdrymYfYIc+yZcumn/cJ/dlvzQVnmSMEVMsCQnfsUgDr8XLmzJlg7/xEVQZP7+OhFVsbXsGFqhubNxS4efOm4B2eyDlbqs3eUNaeUdh8zdr6mZfQn/32ZIs9nhBQ7YkFPaECVIAKUAEqQAWoABUQEQIq04AKUAEqQAWoABWgAlTAKgUIqFaFg85QASpABagAFaACVIAKEFCZA1SAClABKkAFqAAVoAJWKUBAtSocdIYKUAEqQAWoABWgAlSAgMocoAJUwDUFLl68GL2xgWudutQRXh+Dli5dOpd6ZDduKXDjxg3BD16x5tWGawM5iFdnBdqwA96mTZukcOHCuqFI7Hb27Fn9lU2vBgt0bDyOCtxNAQLq3RTi36kAFbirAnhJPnaa+vHHH/XYfPny6Xtc//u///uutgl1QLFixQQ3fGztGa4GCAFI4GX8n376qWunKVu2rGDf982bN7vWJzpasGCB9tm7d+94+8X5sSMXduYKR8POWoMGDZLjx4/HC3Dx+fHFF1/ou1ybN28ekotuaAwfxo0bp2No1qyZ/mD3ObxKCe9RfvbZZ2XAgAEB+zd9+nTdrACvicqSJcsdduPHj5e33npL9u/f72m4D1gwHugpBQiongonB0MFEl4BvEcTW2Vis4MXX3xR0qRJI2PHjlVHsOf8I488kvBO+TnjsGHDBL526dIlbP5cuHBBIQnnwO5jbjXoC7h2e+tbbP+L7X5v374d7ztQAfeI67p169waUox+AMqA3z59+uhuVnG1+PzAe1xRacTub6E0U42XL1+uX8iwiQUqmrgeVqxYIdhOFrvRYY937G5WqFChgN2rVq2abrM8Y8YMvzaXLl3SLZk/+ugjBVU2KuAlBQioXoomx0IFEkGBb7/9Vndeef7556P3ccc2rdg+EzfNli1bypdffqkVMuxsU65cOd1GFkDw8ccfyzfffCOVKlXS7V2feuopqVevXvR2m7B7+umnpXbt2nrTxzQw9oxv06aN7mp17733yogRI7Q/VJnQz//8z/9otQ8+Va9eXcEHx3/33Xdy/fp1vdmvX79ePvnkE/0ddusCGP3Xf/2XTsGimjh16lT97xo1agjAFhUx7PIFkENbuXKlAB7wN99p6diAiuNQVcOOOc8884z+N84/ceJE9RlVO+gya9Ys/YF/2KELWgBkMJaSJUuqVv4ANVD94AeqlNAfkPfOO+8IpofbtWunO+g899xzEhUVpeNyjqtatapqgf3cYXP58mXdOQwAiLGjQg54chr2MEcf//u//6s7UmFL3F69egn6qVKlitSqVUs6deokY8aM0Z+0adNK9+7d9UvN559/rmPG9rmoQH/wwQfa//3336/xRwwBgIiVPz8Qh3fffVfuu+8+rV7369cv6CvBV2NsloH8QtywJASzAxhfqlSpNF+RL6iWv/LKK+rzZ599JkuXLlUfjh07pjvBYVeiDh06aH5iDMinDRs2BJzzBw8elIceekhwfSHnsQUngBf+ILfz5s2rfaNiDM2Re9xoIeiw08BiBQioFgeHrlGBpKAAKoXY033kyJGCilzshqoRKlvYUx2QMnjwYAUPTLW/+uqragdAwg0fSwQAGYBBgBpsULVDdQzQALhNkSKFrF69WjC9CfB58MEHFUQByB07dtQfgNEDDzygrmC5AYAIe80DbuDPo48+qn8DdACkHPibMGGCtG3bVqdmAaUAD4AKwBKQhqoYzoMGKJg2bZo0adIkesi+gIq+MZYKFSoo6AIK4Sf2ugeYAv4w3Zs/f36tGmKqvXLlyrq9LiqwALRTp04p8KAK5w9QA9Fv7dq10VVsACHAHAA1e/ZsBSho+dVXXykoQkvo3LlzZ62CA6wWLlwYPXbs7Y4vAvgCgiUMgEGnTZ48WeMGSAX4A8DRP/QBnAP6sRSkb9++CpA4L7YQxr/oz5niP3DggMYZW8ECxBATtPPnz6sOiEFsPxCbF154QfMKeYO8cBq+KKH/2A3+Ir+c5guogD74C1hHvsD+66+/1i9KAGs05Amm4FHFhEb4coRlGIB2Z2oeuf76668rVAJeMe5AYoacR14C3I8ePao6Ih7IX3wpQ24WKVJEod3RHdoil9iogFcUIKB6JZIcBxVIJAUAWW+//bbewBs2bHiHFwAyQMlvv/2m1ThMRwLOfv31V51eBqCimocbLMANVUFABYABsIT96gGomMoExGE9HyqkNWvWVMhCRQ99wR5QASCELwBUgC8qXgAy9IcbPMAY1cnff/9dq6aAGVTCUJECIKLKBRjC2kFUCn/++WeFMsAM/MQWiIcOHdLjAeaAEKf5AiqqwZjWxQ/OB6DGeADipUuXVp9QNQVoDB06VOrXr68+A86wVhHVOwA7Kmio2MUHqPHph3WdqGguWbJEgRfgiIaK3r/+9a/oKX6MG2PEGkpUvVG1AxxBQ2iHfgDLWO+IZRv4EgCIchr+DkBELgDYsJwCPy+//LKCHs6N2EBbfJEBiE6aNEn7uXXrVjSgAjAB9zgPKoj4UoMlAA6gxuVHXFP8GA8q5rEbINx3ut0BVAA9vlggbqh2QydUyevUqaNffhAb+A2gB6Tjiw/Gi3xEQzX3jTfeiAZIjBPrTzEbgIq1A6jxxQw6FyhQQL8o4frBDEBcgIq4wqfYX5YS6eOAp6UCrilAQHVNSnZEBSJTAadahKoWKpVomIoEuOJGDdDCjdwBDgASpnpxDG6qAFSA4969e7VShxs8QACwihs1gNABVKzBxDQ71nkC5DAVjJs/psFRvQMIP/HEE9GA6gtRvoAKwAQwABABqgASgA+g0oFQjAPLDebOnav+YXrZWYcJv3LlyhUvoGKKFyAOyEAV12mANUAY/sU4oQf6w/hRsQTEAZSd1rhxY4W8+AA1Pv3wxaBgwYLaHap5AHpULWMDKqaQ69atq+slGzRooLqfO3dOihYtqlP7ztgBrVhnCf8HDhwYI+mhMRq+MADiEBOMBw3VPnyxQGUUoOY0QDH8cSqo+IKCyjW+mOBYJwYOoMblR1yAiql3aBu7AXwRQ6c5gIp8xXmdfAZkY3kJ/g6gR6V2zpw5+i8q64ghABVVclRVAZKIufPQFqqhyFH8P6rxDqDGFzN80UK12Fmz6qwVhgZYAoEva/AHmqJaDTgmoEbm56+XR01A9XJ0OTYqkAAKXLlyRQEMaxlxg8Z/AzDQsOYRgASYxE0b4IN1qbjRAnQAqoECKqqPuMkDnABGADvACs4JGMLUKyq4vhVUVPNQgUJzABUVMkxlozrav39/rQICCDC1jkodpsHxu5w5c+raQ0xR4+bv+4BOIIAKYMGUPsYNwEClFVVTwA3GAB/QnLW7AGM8qQ2YRNUP4A9fMb0MmAoVUDGNjiUWgCP4AQjGtPSff/6pU/moYqNKjEouwAeAj6lofLnA/+NLge/Y4wNUVD6dtaComDrT4VhWgEojvgDgXKgKQ2+METYALQdQAWVYEgB/UFV24hcIoMJfLDF4+OGHozMfkIxp8NjNeYApNqBivFhagHWv+CIBP1H1xxcaTP1jOQa+0KB6ji9kyEsAKq4D5B/iB+2wHhTQjDyDDgBZfPkKBFCRN9DBeWuCM+uAL2D40oB+oBF0wzIAxBHXmu+4E+DS5ymoQFgVIKCGVV52TgUiQwGsC8TrcHAzR8MNHNOjqOxgSh5wBBB1mrMcAL/Hgx7+qkmAGdz8nQoqHiBCAwgDolAJxDQ8qqc4DmCAahf6WrNmjU5DxwZUB/JwUweEoi/4Cj8AvYAqVMZQ0UNDpRAwhSqiL6Q509mxp/hjv2YK1UCs0XXOg+UGzqu3AC6AR1RxAWJoWNcKOEQFEs1Z5xnXK5AC1c+ZJsdYoR3Gh3NguQPihoY4Ocs1nBhi7AB+37H/8ccfuhTBXwUVQI0vI85DV1g+sWzZMtm6datqiPzA8g18EUDDNDq+XGB9LgAVVVNUK/FFBEAP4ATUI7+gLeDaqaDG9gP9AoSdCqeTa4g58iJ2w7pQrGd2mq/GWN6AMSBuaJhCR2UZ1UsAMHQCEAKiUXGFTvgi4ZvjsEP+A16dijiA8m4xQ77jS4Cz1AL9YPkK4gB/cE4cg8ozqsMAXnzpgXZsVMBLChBQvRRNjoUKJLICgAZU51BFxRPPvg3rM3GDxdpNTPUG01CJwvpVgA1uxFjr6DyxDLDCDR3QFMxTzLDDVD8g1BdU4BcqpHjgBVVOQLBJw/pKnAdjAGwF0nB81qxZXX23JWKDqevYPqCSircjOC+CR5wA6lj/Gai/gYzJ9xj0D839vWAeU+J4mwAq0FgmgWom8gZQdrcGGMUSEDde+I+44Ul656EknBsaotqOXMRDUaiUYkof60yR13E1ZykA8tR3WcHdxuP7d+Sr8z5UJycxXpwXVXpn1iKYPnksFbBZAQKqzdGhb1SACqgCDqBivR2btxVAdRrVcHzRcVrsNwYklgKAX6wNdSrs8MP39Wpx+YUvO6h8omKM6Xq3GuAYb3xAhRVfPtiogJcUIKB6KZocCxXwqAJ4mh1VMechHI8Ok8P6jwKo6mIJwpkzZxTs8ES7LQ2VVaxTxSYUqDLjAbJAquwAb6yjdV5x5sZ48OAhzh1f9daN87APKpAYChBQE0N1npMKUAEqQAWoABWgAlQgTgUIqEwOKkAFqAAVoAJUgApQAasUIKBaFQ46QwWoABWgAlSAClABKvD/AMEkf1+E3FeQAAAAAElFTkSuQmCC",
"text/plain": [
"<VegaLite 4 object>\n",
"\n",
"If you see this message, it means the renderer has not been properly enabled\n",
"for the frontend that you are using. For more information, see\n",
"https://altair-viz.github.io/user_guide/troubleshooting.html\n"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"blocking_rule = \"l.first_name = r.first_name and l.surname = r.surname\"\n",
"training_session_names = linker.estimate_parameters_using_expectation_maximisation(blocking_rule)\n",
"training_session_names.match_weights_interactive_history_chart()"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"\n",
"----- Starting EM training session -----\n",
"\n",
"Estimating the m probabilities of the model by blocking on:\n",
"l.dob = r.dob\n",
"\n",
"Parameter estimates will be made for the following comparison(s):\n",
" - first_name\n",
" - surname\n",
" - postcode_fake\n",
" - birth_place\n",
" - occupation\n",
"\n",
"Parameter estimates cannot be made for the following comparison(s) since they are used in the blocking rules: \n",
" - dob\n",
"\n",
"Iteration 1: Largest change in params was -0.312 in the m_probability of first_name, level `Exact match`\n",
"Iteration 2: Largest change in params was -0.0708 in the m_probability of first_name, level `Exact match`\n",
"Iteration 3: Largest change in params was -0.0115 in the m_probability of surname, level `Exact match`\n",
"Iteration 4: Largest change in params was -0.00293 in the m_probability of surname, level `Exact match`\n",
"\n",
"EM converged after 4 iterations\n",
"\n",
"Your model is fully trained. All comparisons have at least one estimate for their m and u values\n"
]
},
{
"data": {
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"$schema": "https://vega.github.io/schema/vega-lite/v5.2.json",
"config": {
"header": {
"title": null
},
"mark": {
"tooltip": null
},
"title": {
"anchor": "middle"
},
"view": {
"height": 60,
"width": 400
}
},
"data": {
"values": [
{
"bayes_factor": 0.0017612707196973728,
"bayes_factor_description": "The probability that two random records drawn at random match is 0.002 or one in 568.8 records.This is equivalent to a starting match weight of -9.149.",
"comparison_name": "probability_two_random_records_match",
"comparison_sort_order": -1,
"comparison_vector_value": 0,
"has_tf_adjustments": false,
"is_null_level": false,
"iteration": 0,
"label_for_charts": "",
"log2_bayes_factor": -9.149167606073906,
"m_probability": null,
"m_probability_description": null,
"max_comparison_vector_value": 0,
"probability_two_random_records_match": 0.056726986814442375,
"sql_condition": null,
"tf_adjustment_column": null,
"tf_adjustment_weight": null,
"u_probability": null,
"u_probability_description": null
},
{
"bayes_factor": 72.76983736674188,
"bayes_factor_description": "If comparison level is `exact match` then comparison is 72.77 times more likely to be a match",
"comparison_name": "first_name",
"comparison_sort_order": 0,
"comparison_vector_value": 3,
"has_tf_adjustments": false,
"is_null_level": false,
"iteration": 0,
"label_for_charts": "Exact match",
"log2_bayes_factor": 6.185268681319293,
"m_probability": 0.95,
"m_probability_description": "Amongst matching record comparisons, 95.00% of records are in the exact match comparison level",
"max_comparison_vector_value": 3,
"probability_two_random_records_match": 0.056726986814442375,
"sql_condition": "first_name_l = first_name_r",
"tf_adjustment_column": null,
"tf_adjustment_weight": 1,
"u_probability": 0.01305485946343725,
"u_probability_description": "Amongst non-matching record comparisons, 1.31% of records are in the exact match comparison level"
},
{
"bayes_factor": 39.552571520411476,
"bayes_factor_description": "If comparison level is `jaccard >= 0.9` then comparison is 39.55 times more likely to be a match",
"comparison_name": "first_name",
"comparison_sort_order": 0,
"comparison_vector_value": 2,
"has_tf_adjustments": false,
"is_null_level": false,
"iteration": 0,
"label_for_charts": "jaccard >= 0.9",
"log2_bayes_factor": 5.305699589816577,
"m_probability": 0.01666666666666668,
"m_probability_description": "Amongst matching record comparisons, 1.67% of records are in the jaccard >= 0.9 comparison level",
"max_comparison_vector_value": 3,
"probability_two_random_records_match": 0.056726986814442375,
"sql_condition": "jaccard(first_name_l, first_name_r) >= 0.9",
"tf_adjustment_column": null,
"tf_adjustment_weight": 1,
"u_probability": 0.00042138010313857066,
"u_probability_description": "Amongst non-matching record comparisons, 0.04% of records are in the jaccard >= 0.9 comparison level"
},
{
"bayes_factor": 0.46763749064140164,
"bayes_factor_description": "If comparison level is `jaccard >= 0.5` then comparison is 2.14 times less likely to be a match",
"comparison_name": "first_name",
"comparison_sort_order": 0,
"comparison_vector_value": 1,
"has_tf_adjustments": false,
"is_null_level": false,
"iteration": 0,
"label_for_charts": "jaccard >= 0.5",
"log2_bayes_factor": -1.096537499076254,
"m_probability": 0.01666666666666668,
"m_probability_description": "Amongst matching record comparisons, 1.67% of records are in the jaccard >= 0.5 comparison level",
"max_comparison_vector_value": 3,
"probability_two_random_records_match": 0.056726986814442375,
"sql_condition": "jaccard(first_name_l, first_name_r) >= 0.5",
"tf_adjustment_column": null,
"tf_adjustment_weight": 1,
"u_probability": 0.035640142204610314,
"u_probability_description": "Amongst non-matching record comparisons, 3.56% of records are in the jaccard >= 0.5 comparison level"
},
{
"bayes_factor": 0.017527556839932994,
"bayes_factor_description": "If comparison level is `all other comparisons` then comparison is 57.05 times less likely to be a match",
"comparison_name": "first_name",
"comparison_sort_order": 0,
"comparison_vector_value": 0,
"has_tf_adjustments": false,
"is_null_level": false,
"iteration": 0,
"label_for_charts": "All other comparisons",
"log2_bayes_factor": -5.834231276424401,
"m_probability": 0.01666666666666668,
"m_probability_description": "Amongst matching record comparisons, 1.67% of records are in the all other comparisons comparison level",
"max_comparison_vector_value": 3,
"probability_two_random_records_match": 0.056726986814442375,
"sql_condition": "ELSE",
"tf_adjustment_column": null,
"tf_adjustment_weight": 1,
"u_probability": 0.9508836182288138,
"u_probability_description": "Amongst non-matching record comparisons, 95.09% of records are in the all other comparisons comparison level"
},
{
"bayes_factor": 1201.8453768690874,
"bayes_factor_description": "If comparison level is `exact match` then comparison is 1,201.85 times more likely to be a match",
"comparison_name": "surname",
"comparison_sort_order": 1,
"comparison_vector_value": 3,
"has_tf_adjustments": false,
"is_null_level": false,
"iteration": 0,
"label_for_charts": "Exact match",
"log2_bayes_factor": 10.231035583062827,
"m_probability": 0.95,
"m_probability_description": "Amongst matching record comparisons, 95.00% of records are in the exact match comparison level",
"max_comparison_vector_value": 3,
"probability_two_random_records_match": 0.056726986814442375,
"sql_condition": "surname_l = surname_r",
"tf_adjustment_column": null,
"tf_adjustment_weight": 1,
"u_probability": 0.000790451099853488,
"u_probability_description": "Amongst non-matching record comparisons, 0.08% of records are in the exact match comparison level"
},
{
"bayes_factor": 239.91516203703725,
"bayes_factor_description": "If comparison level is `jaccard >= 0.9` then comparison is 239.92 times more likely to be a match",
"comparison_name": "surname",
"comparison_sort_order": 1,
"comparison_vector_value": 2,
"has_tf_adjustments": false,
"is_null_level": false,
"iteration": 0,
"label_for_charts": "jaccard >= 0.9",
"log2_bayes_factor": 7.906380524998534,
"m_probability": 0.01666666666666668,
"m_probability_description": "Amongst matching record comparisons, 1.67% of records are in the jaccard >= 0.9 comparison level",
"max_comparison_vector_value": 3,
"probability_two_random_records_match": 0.056726986814442375,
"sql_condition": "jaccard(surname_l, surname_r) >= 0.9",
"tf_adjustment_column": null,
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"u_probability": 6.946900114672094e-05,
"u_probability_description": "Amongst non-matching record comparisons, 0.01% of records are in the jaccard >= 0.9 comparison level"
},
{
"bayes_factor": 0.43618460230584577,
"bayes_factor_description": "If comparison level is `jaccard >= 0.5` then comparison is 2.29 times less likely to be a match",
"comparison_name": "surname",
"comparison_sort_order": 1,
"comparison_vector_value": 1,
"has_tf_adjustments": false,
"is_null_level": false,
"iteration": 0,
"label_for_charts": "jaccard >= 0.5",
"log2_bayes_factor": -1.196989252393987,
"m_probability": 0.01666666666666668,
"m_probability_description": "Amongst matching record comparisons, 1.67% of records are in the jaccard >= 0.5 comparison level",
"max_comparison_vector_value": 3,
"probability_two_random_records_match": 0.056726986814442375,
"sql_condition": "jaccard(surname_l, surname_r) >= 0.5",
"tf_adjustment_column": null,
"tf_adjustment_weight": 1,
"u_probability": 0.038210121536982354,
"u_probability_description": "Amongst non-matching record comparisons, 3.82% of records are in the jaccard >= 0.5 comparison level"
},
{
"bayes_factor": 0.01734430956349447,
"bayes_factor_description": "If comparison level is `all other comparisons` then comparison is 57.66 times less likely to be a match",
"comparison_name": "surname",
"comparison_sort_order": 1,
"comparison_vector_value": 0,
"has_tf_adjustments": false,
"is_null_level": false,
"iteration": 0,
"label_for_charts": "All other comparisons",
"log2_bayes_factor": -5.8493937783316525,
"m_probability": 0.01666666666666668,
"m_probability_description": "Amongst matching record comparisons, 1.67% of records are in the all other comparisons comparison level",
"max_comparison_vector_value": 3,
"probability_two_random_records_match": 0.056726986814442375,
"sql_condition": "ELSE",
"tf_adjustment_column": null,
"tf_adjustment_weight": 1,
"u_probability": 0.9609299583620174,
"u_probability_description": "Amongst non-matching record comparisons, 96.09% of records are in the all other comparisons comparison level"
},
{
"bayes_factor": 4392.5096861589245,
"bayes_factor_description": "If comparison level is `exact match` then comparison is 4,392.51 times more likely to be a match",
"comparison_name": "postcode_fake",
"comparison_sort_order": 2,
"comparison_vector_value": 2,
"has_tf_adjustments": false,
"is_null_level": false,
"iteration": 0,
"label_for_charts": "Exact match",
"log2_bayes_factor": 12.100829752263532,
"m_probability": 0.6789115078905416,
"m_probability_description": "Amongst matching record comparisons, 67.89% of records are in the exact match comparison level",
"max_comparison_vector_value": 2,
"probability_two_random_records_match": 0.056726986814442375,
"sql_condition": "postcode_fake_l = postcode_fake_r",
"tf_adjustment_column": null,
"tf_adjustment_weight": 1,
"u_probability": 0.00015456118629170805,
"u_probability_description": "Amongst non-matching record comparisons, 0.02% of records are in the exact match comparison level"
},
{
"bayes_factor": 227.80086824722986,
"bayes_factor_description": "If comparison level is `levenshtein <= 2` then comparison is 227.80 times more likely to be a match",
"comparison_name": "postcode_fake",
"comparison_sort_order": 2,
"comparison_vector_value": 1,
"has_tf_adjustments": false,
"is_null_level": false,
"iteration": 0,
"label_for_charts": "levenshtein <= 2",
"log2_bayes_factor": 7.8316294355634435,
"m_probability": 0.1397856696656179,
"m_probability_description": "Amongst matching record comparisons, 13.98% of records are in the levenshtein <= 2 comparison level",
"max_comparison_vector_value": 2,
"probability_two_random_records_match": 0.056726986814442375,
"sql_condition": "levenshtein(postcode_fake_l, postcode_fake_r) <= 2",
"tf_adjustment_column": null,
"tf_adjustment_weight": 1,
"u_probability": 0.0006136309784118558,
"u_probability_description": "Amongst non-matching record comparisons, 0.06% of records are in the levenshtein <= 2 comparison level"
},
{
"bayes_factor": 0.1814422049238122,
"bayes_factor_description": "If comparison level is `all other comparisons` then comparison is 5.51 times less likely to be a match",
"comparison_name": "postcode_fake",
"comparison_sort_order": 2,
"comparison_vector_value": 0,
"has_tf_adjustments": false,
"is_null_level": false,
"iteration": 0,
"label_for_charts": "All other comparisons",
"log2_bayes_factor": -2.462418017454674,
"m_probability": 0.18130282244364318,
"m_probability_description": "Amongst matching record comparisons, 18.13% of records are in the all other comparisons comparison level",
"max_comparison_vector_value": 2,
"probability_two_random_records_match": 0.056726986814442375,
"sql_condition": "ELSE",
"tf_adjustment_column": null,
"tf_adjustment_weight": 1,
"u_probability": 0.9992318078352964,
"u_probability_description": "Amongst non-matching record comparisons, 99.92% of records are in the all other comparisons comparison level"
},
{
"bayes_factor": 153.8672720379109,
"bayes_factor_description": "If comparison level is `exact match` then comparison is 153.87 times more likely to be a match",
"comparison_name": "birth_place",
"comparison_sort_order": 3,
"comparison_vector_value": 1,
"has_tf_adjustments": false,
"is_null_level": false,
"iteration": 0,
"label_for_charts": "Exact match",
"log2_bayes_factor": 7.26554258914858,
"m_probability": 0.8294000291540573,
"m_probability_description": "Amongst matching record comparisons, 82.94% of records are in the exact match comparison level",
"max_comparison_vector_value": 1,
"probability_two_random_records_match": 0.056726986814442375,
"sql_condition": "birth_place_l = birth_place_r",
"tf_adjustment_column": null,
"tf_adjustment_weight": 1,
"u_probability": 0.005390360264200329,
"u_probability_description": "Amongst non-matching record comparisons, 0.54% of records are in the exact match comparison level"
},
{
"bayes_factor": 0.17152454996427302,
"bayes_factor_description": "If comparison level is `all other comparisons` then comparison is 5.83 times less likely to be a match",
"comparison_name": "birth_place",
"comparison_sort_order": 3,
"comparison_vector_value": 0,
"has_tf_adjustments": false,
"is_null_level": false,
"iteration": 0,
"label_for_charts": "All other comparisons",
"log2_bayes_factor": -2.5435130136675363,
"m_probability": 0.17059997084581074,
"m_probability_description": "Amongst matching record comparisons, 17.06% of records are in the all other comparisons comparison level",
"max_comparison_vector_value": 1,
"probability_two_random_records_match": 0.056726986814442375,
"sql_condition": "ELSE",
"tf_adjustment_column": null,
"tf_adjustment_weight": 1,
"u_probability": 0.9946096397357996,
"u_probability_description": "Amongst non-matching record comparisons, 99.46% of records are in the all other comparisons comparison level"
},
{
"bayes_factor": 26.062190740951976,
"bayes_factor_description": "If comparison level is `exact match` then comparison is 26.06 times more likely to be a match",
"comparison_name": "occupation",
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"comparison_vector_value": 1,
"has_tf_adjustments": false,
"is_null_level": false,
"iteration": 0,
"label_for_charts": "Exact match",
"log2_bayes_factor": 4.703886454244968,
"m_probability": 0.9071799013778532,
"m_probability_description": "Amongst matching record comparisons, 90.72% of records are in the exact match comparison level",
"max_comparison_vector_value": 1,
"probability_two_random_records_match": 0.056726986814442375,
"sql_condition": "occupation_l = occupation_r",
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"tf_adjustment_weight": 1,
"u_probability": 0.03480827496026209,
"u_probability_description": "Amongst non-matching record comparisons, 3.48% of records are in the exact match comparison level"
},
{
"bayes_factor": 0.09616752424856101,
"bayes_factor_description": "If comparison level is `all other comparisons` then comparison is 10.40 times less likely to be a match",
"comparison_name": "occupation",
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"comparison_vector_value": 0,
"has_tf_adjustments": false,
"is_null_level": false,
"iteration": 0,
"label_for_charts": "All other comparisons",
"log2_bayes_factor": -3.3783064113274013,
"m_probability": 0.09282009862226943,
"m_probability_description": "Amongst matching record comparisons, 9.28% of records are in the all other comparisons comparison level",
"max_comparison_vector_value": 1,
"probability_two_random_records_match": 0.056726986814442375,
"sql_condition": "ELSE",
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"tf_adjustment_weight": 1,
"u_probability": 0.9651917250397379,
"u_probability_description": "Amongst non-matching record comparisons, 96.52% of records are in the all other comparisons comparison level"
},
{
"bayes_factor": 0.049087564684904876,
"bayes_factor_description": "The probability that two random records drawn at random match is 0.047 or one in 21.4 records.This is equivalent to a starting match weight of -4.348.",
"comparison_name": "probability_two_random_records_match",
"comparison_sort_order": -1,
"comparison_vector_value": 0,
"has_tf_adjustments": false,
"is_null_level": false,
"iteration": 1,
"label_for_charts": "",
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"max_comparison_vector_value": 0,
"probability_two_random_records_match": 0.056726986814442375,
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},
{
"bayes_factor": 48.897192303824504,
"bayes_factor_description": "If comparison level is `exact match` then comparison is 48.90 times more likely to be a match",
"comparison_name": "first_name",
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"comparison_vector_value": 3,
"has_tf_adjustments": false,
"is_null_level": false,
"iteration": 1,
"label_for_charts": "Exact match",
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"m_probability": 0.6383459736830944,
"m_probability_description": "Amongst matching record comparisons, 63.83% of records are in the exact match comparison level",
"max_comparison_vector_value": 3,
"probability_two_random_records_match": 0.056726986814442375,
"sql_condition": "first_name_l = first_name_r",
"tf_adjustment_column": null,
"tf_adjustment_weight": 1,
"u_probability": 0.01305485946343725,
"u_probability_description": "Amongst non-matching record comparisons, 1.31% of records are in the exact match comparison level"
},
{
"bayes_factor": 19.59741231689835,
"bayes_factor_description": "If comparison level is `jaccard >= 0.9` then comparison is 19.60 times more likely to be a match",
"comparison_name": "first_name",
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"comparison_vector_value": 2,
"has_tf_adjustments": false,
"is_null_level": false,
"iteration": 1,
"label_for_charts": "jaccard >= 0.9",
"log2_bayes_factor": 4.292591265348278,
"m_probability": 0.008257959623343721,
"m_probability_description": "Amongst matching record comparisons, 0.83% of records are in the jaccard >= 0.9 comparison level",
"max_comparison_vector_value": 3,
"probability_two_random_records_match": 0.056726986814442375,
"sql_condition": "jaccard(first_name_l, first_name_r) >= 0.9",
"tf_adjustment_column": null,
"tf_adjustment_weight": 1,
"u_probability": 0.00042138010313857066,
"u_probability_description": "Amongst non-matching record comparisons, 0.04% of records are in the jaccard >= 0.9 comparison level"
},
{
"bayes_factor": 5.837932905302266,
"bayes_factor_description": "If comparison level is `jaccard >= 0.5` then comparison is 5.84 times more likely to be a match",
"comparison_name": "first_name",
"comparison_sort_order": 0,
"comparison_vector_value": 1,
"has_tf_adjustments": false,
"is_null_level": false,
"iteration": 1,
"label_for_charts": "jaccard >= 0.5",
"log2_bayes_factor": 2.5454576302057292,
"m_probability": 0.2080647589259466,
"m_probability_description": "Amongst matching record comparisons, 20.81% of records are in the jaccard >= 0.5 comparison level",
"max_comparison_vector_value": 3,
"probability_two_random_records_match": 0.056726986814442375,
"sql_condition": "jaccard(first_name_l, first_name_r) >= 0.5",
"tf_adjustment_column": null,
"tf_adjustment_weight": 1,
"u_probability": 0.035640142204610314,
"u_probability_description": "Amongst non-matching record comparisons, 3.56% of records are in the jaccard >= 0.5 comparison level"
},
{
"bayes_factor": 0.1528381654500091,
"bayes_factor_description": "If comparison level is `all other comparisons` then comparison is 6.54 times less likely to be a match",
"comparison_name": "first_name",
"comparison_sort_order": 0,
"comparison_vector_value": 0,
"has_tf_adjustments": false,
"is_null_level": false,
"iteration": 1,
"label_for_charts": "All other comparisons",
"log2_bayes_factor": -2.7099232489600813,
"m_probability": 0.14533130776655875,
"m_probability_description": "Amongst matching record comparisons, 14.53% of records are in the all other comparisons comparison level",
"max_comparison_vector_value": 3,
"probability_two_random_records_match": 0.056726986814442375,
"sql_condition": "ELSE",
"tf_adjustment_column": null,
"tf_adjustment_weight": 1,
"u_probability": 0.9508836182288138,
"u_probability_description": "Amongst non-matching record comparisons, 95.09% of records are in the all other comparisons comparison level"
},
{
"bayes_factor": 1080.5633578575753,
"bayes_factor_description": "If comparison level is `exact match` then comparison is 1,080.56 times more likely to be a match",
"comparison_name": "surname",
"comparison_sort_order": 1,
"comparison_vector_value": 3,
"has_tf_adjustments": false,
"is_null_level": false,
"iteration": 1,
"label_for_charts": "Exact match",
"log2_bayes_factor": 10.07756795046165,
"m_probability": 0.8541324946798985,
"m_probability_description": "Amongst matching record comparisons, 85.41% of records are in the exact match comparison level",
"max_comparison_vector_value": 3,
"probability_two_random_records_match": 0.056726986814442375,
"sql_condition": "surname_l = surname_r",
"tf_adjustment_column": null,
"tf_adjustment_weight": 1,
"u_probability": 0.000790451099853488,
"u_probability_description": "Amongst non-matching record comparisons, 0.08% of records are in the exact match comparison level"
},
{
"bayes_factor": 393.8236431301664,
"bayes_factor_description": "If comparison level is `jaccard >= 0.9` then comparison is 393.82 times more likely to be a match",
"comparison_name": "surname",
"comparison_sort_order": 1,
"comparison_vector_value": 2,
"has_tf_adjustments": false,
"is_null_level": false,
"iteration": 1,
"label_for_charts": "jaccard >= 0.9",
"log2_bayes_factor": 8.621405915546271,
"m_probability": 0.027358535116215346,
"m_probability_description": "Amongst matching record comparisons, 2.74% of records are in the jaccard >= 0.9 comparison level",
"max_comparison_vector_value": 3,
"probability_two_random_records_match": 0.056726986814442375,
"sql_condition": "jaccard(surname_l, surname_r) >= 0.9",
"tf_adjustment_column": null,
"tf_adjustment_weight": 1,
"u_probability": 6.946900114672094e-05,
"u_probability_description": "Amongst non-matching record comparisons, 0.01% of records are in the jaccard >= 0.9 comparison level"
},
{
"bayes_factor": 2.414022904430979,
"bayes_factor_description": "If comparison level is `jaccard >= 0.5` then comparison is 2.41 times more likely to be a match",
"comparison_name": "surname",
"comparison_sort_order": 1,
"comparison_vector_value": 1,
"has_tf_adjustments": false,
"is_null_level": false,
"iteration": 1,
"label_for_charts": "jaccard >= 0.5",
"log2_bayes_factor": 1.271439364556988,
"m_probability": 0.09224010857136684,
"m_probability_description": "Amongst matching record comparisons, 9.22% of records are in the jaccard >= 0.5 comparison level",
"max_comparison_vector_value": 3,
"probability_two_random_records_match": 0.056726986814442375,
"sql_condition": "jaccard(surname_l, surname_r) >= 0.5",
"tf_adjustment_column": null,
"tf_adjustment_weight": 1,
"u_probability": 0.038210121536982354,
"u_probability_description": "Amongst non-matching record comparisons, 3.82% of records are in the jaccard >= 0.5 comparison level"
},
{
"bayes_factor": 0.027336916081031653,
"bayes_factor_description": "If comparison level is `all other comparisons` then comparison is 36.58 times less likely to be a match",
"comparison_name": "surname",
"comparison_sort_order": 1,
"comparison_vector_value": 0,
"has_tf_adjustments": false,
"is_null_level": false,
"iteration": 1,
"label_for_charts": "All other comparisons",
"log2_bayes_factor": -5.193005690267132,
"m_probability": 0.02626886163149171,
"m_probability_description": "Amongst matching record comparisons, 2.63% of records are in the all other comparisons comparison level",
"max_comparison_vector_value": 3,
"probability_two_random_records_match": 0.056726986814442375,
"sql_condition": "ELSE",
"tf_adjustment_column": null,
"tf_adjustment_weight": 1,
"u_probability": 0.9609299583620174,
"u_probability_description": "Amongst non-matching record comparisons, 96.09% of records are in the all other comparisons comparison level"
},
{
"bayes_factor": 4796.257521719333,
"bayes_factor_description": "If comparison level is `exact match` then comparison is 4,796.26 times more likely to be a match",
"comparison_name": "postcode_fake",
"comparison_sort_order": 2,
"comparison_vector_value": 2,
"has_tf_adjustments": false,
"is_null_level": false,
"iteration": 1,
"label_for_charts": "Exact match",
"log2_bayes_factor": 12.22769340699496,
"m_probability": 0.7413152523174679,
"m_probability_description": "Amongst matching record comparisons, 74.13% of records are in the exact match comparison level",
"max_comparison_vector_value": 2,
"probability_two_random_records_match": 0.056726986814442375,
"sql_condition": "postcode_fake_l = postcode_fake_r",
"tf_adjustment_column": null,
"tf_adjustment_weight": 1,
"u_probability": 0.00015456118629170805,
"u_probability_description": "Amongst non-matching record comparisons, 0.02% of records are in the exact match comparison level"
},
{
"bayes_factor": 228.05956548874704,
"bayes_factor_description": "If comparison level is `levenshtein <= 2` then comparison is 228.06 times more likely to be a match",
"comparison_name": "postcode_fake",
"comparison_sort_order": 2,
"comparison_vector_value": 1,
"has_tf_adjustments": false,
"is_null_level": false,
"iteration": 1,
"label_for_charts": "levenshtein <= 2",
"log2_bayes_factor": 7.8332668721114445,
"m_probability": 0.13994441430704255,
"m_probability_description": "Amongst matching record comparisons, 13.99% of records are in the levenshtein <= 2 comparison level",
"max_comparison_vector_value": 2,
"probability_two_random_records_match": 0.056726986814442375,
"sql_condition": "levenshtein(postcode_fake_l, postcode_fake_r) <= 2",
"tf_adjustment_column": null,
"tf_adjustment_weight": 1,
"u_probability": 0.0006136309784118558,
"u_probability_description": "Amongst non-matching record comparisons, 0.06% of records are in the levenshtein <= 2 comparison level"
},
{
"bayes_factor": 0.11883161889312746,
"bayes_factor_description": "If comparison level is `all other comparisons` then comparison is 8.42 times less likely to be a match",
"comparison_name": "postcode_fake",
"comparison_sort_order": 2,
"comparison_vector_value": 0,
"has_tf_adjustments": false,
"is_null_level": false,
"iteration": 1,
"label_for_charts": "All other comparisons",
"log2_bayes_factor": -3.073009333235782,
"m_probability": 0.11874033337457472,
"m_probability_description": "Amongst matching record comparisons, 11.87% of records are in the all other comparisons comparison level",
"max_comparison_vector_value": 2,
"probability_two_random_records_match": 0.056726986814442375,
"sql_condition": "ELSE",
"tf_adjustment_column": null,
"tf_adjustment_weight": 1,
"u_probability": 0.9992318078352964,
"u_probability_description": "Amongst non-matching record comparisons, 99.92% of records are in the all other comparisons comparison level"
},
{
"bayes_factor": 163.89094439539855,
"bayes_factor_description": "If comparison level is `exact match` then comparison is 163.89 times more likely to be a match",
"comparison_name": "birth_place",
"comparison_sort_order": 3,
"comparison_vector_value": 1,
"has_tf_adjustments": false,
"is_null_level": false,
"iteration": 1,
"label_for_charts": "Exact match",
"log2_bayes_factor": 7.356592331968195,
"m_probability": 0.8834312343312218,
"m_probability_description": "Amongst matching record comparisons, 88.34% of records are in the exact match comparison level",
"max_comparison_vector_value": 1,
"probability_two_random_records_match": 0.056726986814442375,
"sql_condition": "birth_place_l = birth_place_r",
"tf_adjustment_column": null,
"tf_adjustment_weight": 1,
"u_probability": 0.005390360264200329,
"u_probability_description": "Amongst non-matching record comparisons, 0.54% of records are in the exact match comparison level"
},
{
"bayes_factor": 0.117200518686695,
"bayes_factor_description": "If comparison level is `all other comparisons` then comparison is 8.53 times less likely to be a match",
"comparison_name": "birth_place",
"comparison_sort_order": 3,
"comparison_vector_value": 0,
"has_tf_adjustments": false,
"is_null_level": false,
"iteration": 1,
"label_for_charts": "All other comparisons",
"log2_bayes_factor": -3.092949140271683,
"m_probability": 0.11656876566782257,
"m_probability_description": "Amongst matching record comparisons, 11.66% of records are in the all other comparisons comparison level",
"max_comparison_vector_value": 1,
"probability_two_random_records_match": 0.056726986814442375,
"sql_condition": "ELSE",
"tf_adjustment_column": null,
"tf_adjustment_weight": 1,
"u_probability": 0.9946096397357996,
"u_probability_description": "Amongst non-matching record comparisons, 99.46% of records are in the all other comparisons comparison level"
},
{
"bayes_factor": 26.254519756555755,
"bayes_factor_description": "If comparison level is `exact match` then comparison is 26.25 times more likely to be a match",
"comparison_name": "occupation",
"comparison_sort_order": 4,
"comparison_vector_value": 1,
"has_tf_adjustments": false,
"is_null_level": false,
"iteration": 1,
"label_for_charts": "Exact match",
"log2_bayes_factor": 4.714493901249683,
"m_probability": 0.9138745426358261,
"m_probability_description": "Amongst matching record comparisons, 91.39% of records are in the exact match comparison level",
"max_comparison_vector_value": 1,
"probability_two_random_records_match": 0.056726986814442375,
"sql_condition": "occupation_l = occupation_r",
"tf_adjustment_column": null,
"tf_adjustment_weight": 1,
"u_probability": 0.03480827496026209,
"u_probability_description": "Amongst non-matching record comparisons, 3.48% of records are in the exact match comparison level"
},
{
"bayes_factor": 0.08923145021803135,
"bayes_factor_description": "If comparison level is `all other comparisons` then comparison is 11.21 times less likely to be a match",
"comparison_name": "occupation",
"comparison_sort_order": 4,
"comparison_vector_value": 0,
"has_tf_adjustments": false,
"is_null_level": false,
"iteration": 1,
"label_for_charts": "All other comparisons",
"log2_bayes_factor": -3.4863039025368985,
"m_probability": 0.08612545736373918,
"m_probability_description": "Amongst matching record comparisons, 8.61% of records are in the all other comparisons comparison level",
"max_comparison_vector_value": 1,
"probability_two_random_records_match": 0.056726986814442375,
"sql_condition": "ELSE",
"tf_adjustment_column": null,
"tf_adjustment_weight": 1,
"u_probability": 0.9651917250397379,
"u_probability_description": "Amongst non-matching record comparisons, 96.52% of records are in the all other comparisons comparison level"
},
{
"bayes_factor": 0.058257456912884954,
"bayes_factor_description": "The probability that two random records drawn at random match is 0.055 or one in 18.2 records.This is equivalent to a starting match weight of -4.101.",
"comparison_name": "probability_two_random_records_match",
"comparison_sort_order": -1,
"comparison_vector_value": 0,
"has_tf_adjustments": false,
"is_null_level": false,
"iteration": 2,
"label_for_charts": "",
"log2_bayes_factor": -4.101413464253324,
"m_probability": null,
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"max_comparison_vector_value": 0,
"probability_two_random_records_match": 0.056726986814442375,
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"tf_adjustment_column": null,
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},
{
"bayes_factor": 43.471569394723296,
"bayes_factor_description": "If comparison level is `exact match` then comparison is 43.47 times more likely to be a match",
"comparison_name": "first_name",
"comparison_sort_order": 0,
"comparison_vector_value": 3,
"has_tf_adjustments": false,
"is_null_level": false,
"iteration": 2,
"label_for_charts": "Exact match",
"log2_bayes_factor": 5.442000275093057,
"m_probability": 0.5675152291031725,
"m_probability_description": "Amongst matching record comparisons, 56.75% of records are in the exact match comparison level",
"max_comparison_vector_value": 3,
"probability_two_random_records_match": 0.056726986814442375,
"sql_condition": "first_name_l = first_name_r",
"tf_adjustment_column": null,
"tf_adjustment_weight": 1,
"u_probability": 0.01305485946343725,
"u_probability_description": "Amongst non-matching record comparisons, 1.31% of records are in the exact match comparison level"
},
{
"bayes_factor": 17.89622875042037,
"bayes_factor_description": "If comparison level is `jaccard >= 0.9` then comparison is 17.90 times more likely to be a match",
"comparison_name": "first_name",
"comparison_sort_order": 0,
"comparison_vector_value": 2,
"has_tf_adjustments": false,
"is_null_level": false,
"iteration": 2,
"label_for_charts": "jaccard >= 0.9",
"log2_bayes_factor": 4.161583697109365,
"m_probability": 0.00754111471664359,
"m_probability_description": "Amongst matching record comparisons, 0.75% of records are in the jaccard >= 0.9 comparison level",
"max_comparison_vector_value": 3,
"probability_two_random_records_match": 0.056726986814442375,
"sql_condition": "jaccard(first_name_l, first_name_r) >= 0.9",
"tf_adjustment_column": null,
"tf_adjustment_weight": 1,
"u_probability": 0.00042138010313857066,
"u_probability_description": "Amongst non-matching record comparisons, 0.04% of records are in the jaccard >= 0.9 comparison level"
},
{
"bayes_factor": 6.377889504151217,
"bayes_factor_description": "If comparison level is `jaccard >= 0.5` then comparison is 6.38 times more likely to be a match",
"comparison_name": "first_name",
"comparison_sort_order": 0,
"comparison_vector_value": 1,
"has_tf_adjustments": false,
"is_null_level": false,
"iteration": 2,
"label_for_charts": "jaccard >= 0.5",
"log2_bayes_factor": 2.6730791033609544,
"m_probability": 0.22730888889324097,
"m_probability_description": "Amongst matching record comparisons, 22.73% of records are in the jaccard >= 0.5 comparison level",
"max_comparison_vector_value": 3,
"probability_two_random_records_match": 0.056726986814442375,
"sql_condition": "jaccard(first_name_l, first_name_r) >= 0.5",
"tf_adjustment_column": null,
"tf_adjustment_weight": 1,
"u_probability": 0.035640142204610314,
"u_probability_description": "Amongst non-matching record comparisons, 3.56% of records are in the jaccard >= 0.5 comparison level"
},
{
"bayes_factor": 0.20784327703062858,
"bayes_factor_description": "If comparison level is `all other comparisons` then comparison is 4.81 times less likely to be a match",
"comparison_name": "first_name",
"comparison_sort_order": 0,
"comparison_vector_value": 0,
"has_tf_adjustments": false,
"is_null_level": false,
"iteration": 2,
"label_for_charts": "All other comparisons",
"log2_bayes_factor": -2.2664320120751236,
"m_probability": 0.19763476728741783,
"m_probability_description": "Amongst matching record comparisons, 19.76% of records are in the all other comparisons comparison level",
"max_comparison_vector_value": 3,
"probability_two_random_records_match": 0.056726986814442375,
"sql_condition": "ELSE",
"tf_adjustment_column": null,
"tf_adjustment_weight": 1,
"u_probability": 0.9508836182288138,
"u_probability_description": "Amongst non-matching record comparisons, 95.09% of records are in the all other comparisons comparison level"
},
{
"bayes_factor": 1015.2815584242044,
"bayes_factor_description": "If comparison level is `exact match` then comparison is 1,015.28 times more likely to be a match",
"comparison_name": "surname",
"comparison_sort_order": 1,
"comparison_vector_value": 3,
"has_tf_adjustments": false,
"is_null_level": false,
"iteration": 2,
"label_for_charts": "Exact match",
"log2_bayes_factor": 9.98766415651875,
"m_probability": 0.8025304245173757,
"m_probability_description": "Amongst matching record comparisons, 80.25% of records are in the exact match comparison level",
"max_comparison_vector_value": 3,
"probability_two_random_records_match": 0.056726986814442375,
"sql_condition": "surname_l = surname_r",
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"tf_adjustment_weight": 1,
"u_probability": 0.000790451099853488,
"u_probability_description": "Amongst non-matching record comparisons, 0.08% of records are in the exact match comparison level"
},
{
"bayes_factor": 396.4192119029276,
"bayes_factor_description": "If comparison level is `jaccard >= 0.9` then comparison is 396.42 times more likely to be a match",
"comparison_name": "surname",
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"comparison_vector_value": 2,
"has_tf_adjustments": false,
"is_null_level": false,
"iteration": 2,
"label_for_charts": "jaccard >= 0.9",
"log2_bayes_factor": 8.630883072191754,
"m_probability": 0.027538846686266688,
"m_probability_description": "Amongst matching record comparisons, 2.75% of records are in the jaccard >= 0.9 comparison level",
"max_comparison_vector_value": 3,
"probability_two_random_records_match": 0.056726986814442375,
"sql_condition": "jaccard(surname_l, surname_r) >= 0.9",
"tf_adjustment_column": null,
"tf_adjustment_weight": 1,
"u_probability": 6.946900114672094e-05,
"u_probability_description": "Amongst non-matching record comparisons, 0.01% of records are in the jaccard >= 0.9 comparison level"
},
{
"bayes_factor": 3.218459455452871,
"bayes_factor_description": "If comparison level is `jaccard >= 0.5` then comparison is 3.22 times more likely to be a match",
"comparison_name": "surname",
"comparison_sort_order": 1,
"comparison_vector_value": 1,
"has_tf_adjustments": false,
"is_null_level": false,
"iteration": 2,
"label_for_charts": "jaccard >= 0.5",
"log2_bayes_factor": 1.6863702946094656,
"m_probability": 0.12297772695470426,
"m_probability_description": "Amongst matching record comparisons, 12.30% of records are in the jaccard >= 0.5 comparison level",
"max_comparison_vector_value": 3,
"probability_two_random_records_match": 0.056726986814442375,
"sql_condition": "jaccard(surname_l, surname_r) >= 0.5",
"tf_adjustment_column": null,
"tf_adjustment_weight": 1,
"u_probability": 0.038210121536982354,
"u_probability_description": "Amongst non-matching record comparisons, 3.82% of records are in the jaccard >= 0.5 comparison level"
},
{
"bayes_factor": 0.04886204393315619,
"bayes_factor_description": "If comparison level is `all other comparisons` then comparison is 20.47 times less likely to be a match",
"comparison_name": "surname",
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"has_tf_adjustments": false,
"is_null_level": false,
"iteration": 2,
"label_for_charts": "All other comparisons",
"log2_bayes_factor": -4.35514197597404,
"m_probability": 0.04695300184217085,
"m_probability_description": "Amongst matching record comparisons, 4.70% of records are in the all other comparisons comparison level",
"max_comparison_vector_value": 3,
"probability_two_random_records_match": 0.056726986814442375,
"sql_condition": "ELSE",
"tf_adjustment_column": null,
"tf_adjustment_weight": 1,
"u_probability": 0.9609299583620174,
"u_probability_description": "Amongst non-matching record comparisons, 96.09% of records are in the all other comparisons comparison level"
},
{
"bayes_factor": 4554.491009054013,
"bayes_factor_description": "If comparison level is `exact match` then comparison is 4,554.49 times more likely to be a match",
"comparison_name": "postcode_fake",
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"comparison_vector_value": 2,
"has_tf_adjustments": false,
"is_null_level": false,
"iteration": 2,
"label_for_charts": "Exact match",
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"m_probability": 0.7039475333143067,
"m_probability_description": "Amongst matching record comparisons, 70.39% of records are in the exact match comparison level",
"max_comparison_vector_value": 2,
"probability_two_random_records_match": 0.056726986814442375,
"sql_condition": "postcode_fake_l = postcode_fake_r",
"tf_adjustment_column": null,
"tf_adjustment_weight": 1,
"u_probability": 0.00015456118629170805,
"u_probability_description": "Amongst non-matching record comparisons, 0.02% of records are in the exact match comparison level"
},
{
"bayes_factor": 236.34438544459627,
"bayes_factor_description": "If comparison level is `levenshtein <= 2` then comparison is 236.34 times more likely to be a match",
"comparison_name": "postcode_fake",
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"comparison_vector_value": 1,
"has_tf_adjustments": false,
"is_null_level": false,
"iteration": 2,
"label_for_charts": "levenshtein <= 2",
"log2_bayes_factor": 7.884746782470233,
"m_probability": 0.14502823648251637,
"m_probability_description": "Amongst matching record comparisons, 14.50% of records are in the levenshtein <= 2 comparison level",
"max_comparison_vector_value": 2,
"probability_two_random_records_match": 0.056726986814442375,
"sql_condition": "levenshtein(postcode_fake_l, postcode_fake_r) <= 2",
"tf_adjustment_column": null,
"tf_adjustment_weight": 1,
"u_probability": 0.0006136309784118558,
"u_probability_description": "Amongst non-matching record comparisons, 0.06% of records are in the levenshtein <= 2 comparison level"
},
{
"bayes_factor": 0.1511403350247388,
"bayes_factor_description": "If comparison level is `all other comparisons` then comparison is 6.62 times less likely to be a match",
"comparison_name": "postcode_fake",
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"has_tf_adjustments": false,
"is_null_level": false,
"iteration": 2,
"label_for_charts": "All other comparisons",
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"m_probability": 0.15102423020360212,
"m_probability_description": "Amongst matching record comparisons, 15.10% of records are in the all other comparisons comparison level",
"max_comparison_vector_value": 2,
"probability_two_random_records_match": 0.056726986814442375,
"sql_condition": "ELSE",
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"tf_adjustment_weight": 1,
"u_probability": 0.9992318078352964,
"u_probability_description": "Amongst non-matching record comparisons, 99.92% of records are in the all other comparisons comparison level"
},
{
"bayes_factor": 159.14174316212763,
"bayes_factor_description": "If comparison level is `exact match` then comparison is 159.14 times more likely to be a match",
"comparison_name": "birth_place",
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"comparison_vector_value": 1,
"has_tf_adjustments": false,
"is_null_level": false,
"iteration": 2,
"label_for_charts": "Exact match",
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"m_probability": 0.8578313287167071,
"m_probability_description": "Amongst matching record comparisons, 85.78% of records are in the exact match comparison level",
"max_comparison_vector_value": 1,
"probability_two_random_records_match": 0.056726986814442375,
"sql_condition": "birth_place_l = birth_place_r",
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"tf_adjustment_weight": 1,
"u_probability": 0.005390360264200329,
"u_probability_description": "Amongst non-matching record comparisons, 0.54% of records are in the exact match comparison level"
},
{
"bayes_factor": 0.1429391648788649,
"bayes_factor_description": "If comparison level is `all other comparisons` then comparison is 7.00 times less likely to be a match",
"comparison_name": "birth_place",
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"comparison_vector_value": 0,
"has_tf_adjustments": false,
"is_null_level": false,
"iteration": 2,
"label_for_charts": "All other comparisons",
"log2_bayes_factor": -2.8065268304131155,
"m_probability": 0.14216867128430388,
"m_probability_description": "Amongst matching record comparisons, 14.22% of records are in the all other comparisons comparison level",
"max_comparison_vector_value": 1,
"probability_two_random_records_match": 0.056726986814442375,
"sql_condition": "ELSE",
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"u_probability": 0.9946096397357996,
"u_probability_description": "Amongst non-matching record comparisons, 99.46% of records are in the all other comparisons comparison level"
},
{
"bayes_factor": 25.84810545408953,
"bayes_factor_description": "If comparison level is `exact match` then comparison is 25.85 times more likely to be a match",
"comparison_name": "occupation",
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"comparison_vector_value": 1,
"has_tf_adjustments": false,
"is_null_level": false,
"iteration": 2,
"label_for_charts": "Exact match",
"log2_bayes_factor": 4.691986636466077,
"m_probability": 0.8997279618477987,
"m_probability_description": "Amongst matching record comparisons, 89.97% of records are in the exact match comparison level",
"max_comparison_vector_value": 1,
"probability_two_random_records_match": 0.056726986814442375,
"sql_condition": "occupation_l = occupation_r",
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"tf_adjustment_weight": 1,
"u_probability": 0.03480827496026209,
"u_probability_description": "Amongst non-matching record comparisons, 3.48% of records are in the exact match comparison level"
},
{
"bayes_factor": 0.10388820744244502,
"bayes_factor_description": "If comparison level is `all other comparisons` then comparison is 9.63 times less likely to be a match",
"comparison_name": "occupation",
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"has_tf_adjustments": false,
"is_null_level": false,
"iteration": 2,
"label_for_charts": "All other comparisons",
"log2_bayes_factor": -3.2668961945418773,
"m_probability": 0.10027203815265966,
"m_probability_description": "Amongst matching record comparisons, 10.03% of records are in the all other comparisons comparison level",
"max_comparison_vector_value": 1,
"probability_two_random_records_match": 0.056726986814442375,
"sql_condition": "ELSE",
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"tf_adjustment_weight": 1,
"u_probability": 0.9651917250397379,
"u_probability_description": "Amongst non-matching record comparisons, 96.52% of records are in the all other comparisons comparison level"
},
{
"bayes_factor": 0.059782715566879904,
"bayes_factor_description": "The probability that two random records drawn at random match is 0.056 or one in 17.7 records.This is equivalent to a starting match weight of -4.064.",
"comparison_name": "probability_two_random_records_match",
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"is_null_level": false,
"iteration": 3,
"label_for_charts": "",
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"max_comparison_vector_value": 0,
"probability_two_random_records_match": 0.056726986814442375,
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},
{
"bayes_factor": 42.735343717381475,
"bayes_factor_description": "If comparison level is `exact match` then comparison is 42.74 times more likely to be a match",
"comparison_name": "first_name",
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"comparison_vector_value": 3,
"has_tf_adjustments": false,
"is_null_level": false,
"iteration": 3,
"label_for_charts": "Exact match",
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"m_probability": 0.5579039063521012,
"m_probability_description": "Amongst matching record comparisons, 55.79% of records are in the exact match comparison level",
"max_comparison_vector_value": 3,
"probability_two_random_records_match": 0.056726986814442375,
"sql_condition": "first_name_l = first_name_r",
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"tf_adjustment_weight": 1,
"u_probability": 0.01305485946343725,
"u_probability_description": "Amongst non-matching record comparisons, 1.31% of records are in the exact match comparison level"
},
{
"bayes_factor": 17.68183226607441,
"bayes_factor_description": "If comparison level is `jaccard >= 0.9` then comparison is 17.68 times more likely to be a match",
"comparison_name": "first_name",
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"comparison_vector_value": 2,
"has_tf_adjustments": false,
"is_null_level": false,
"iteration": 3,
"label_for_charts": "jaccard >= 0.9",
"log2_bayes_factor": 4.144195875511176,
"m_probability": 0.007450772303957341,
"m_probability_description": "Amongst matching record comparisons, 0.75% of records are in the jaccard >= 0.9 comparison level",
"max_comparison_vector_value": 3,
"probability_two_random_records_match": 0.056726986814442375,
"sql_condition": "jaccard(first_name_l, first_name_r) >= 0.9",
"tf_adjustment_column": null,
"tf_adjustment_weight": 1,
"u_probability": 0.00042138010313857066,
"u_probability_description": "Amongst non-matching record comparisons, 0.04% of records are in the jaccard >= 0.9 comparison level"
},
{
"bayes_factor": 6.369009438437449,
"bayes_factor_description": "If comparison level is `jaccard >= 0.5` then comparison is 6.37 times more likely to be a match",
"comparison_name": "first_name",
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"comparison_vector_value": 1,
"has_tf_adjustments": false,
"is_null_level": false,
"iteration": 3,
"label_for_charts": "jaccard >= 0.5",
"log2_bayes_factor": 2.6710690099416508,
"m_probability": 0.22699240208841598,
"m_probability_description": "Amongst matching record comparisons, 22.70% of records are in the jaccard >= 0.5 comparison level",
"max_comparison_vector_value": 3,
"probability_two_random_records_match": 0.056726986814442375,
"sql_condition": "jaccard(first_name_l, first_name_r) >= 0.5",
"tf_adjustment_column": null,
"tf_adjustment_weight": 1,
"u_probability": 0.035640142204610314,
"u_probability_description": "Amongst non-matching record comparisons, 3.56% of records are in the jaccard >= 0.5 comparison level"
},
{
"bayes_factor": 0.21837890071471,
"bayes_factor_description": "If comparison level is `all other comparisons` then comparison is 4.58 times less likely to be a match",
"comparison_name": "first_name",
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"comparison_vector_value": 0,
"has_tf_adjustments": false,
"is_null_level": false,
"iteration": 3,
"label_for_charts": "All other comparisons",
"log2_bayes_factor": -2.1950946218929737,
"m_probability": 0.20765291925643434,
"m_probability_description": "Amongst matching record comparisons, 20.77% of records are in the all other comparisons comparison level",
"max_comparison_vector_value": 3,
"probability_two_random_records_match": 0.056726986814442375,
"sql_condition": "ELSE",
"tf_adjustment_column": null,
"tf_adjustment_weight": 1,
"u_probability": 0.9508836182288138,
"u_probability_description": "Amongst non-matching record comparisons, 95.09% of records are in the all other comparisons comparison level"
},
{
"bayes_factor": 1000.6747428406501,
"bayes_factor_description": "If comparison level is `exact match` then comparison is 1,000.67 times more likely to be a match",
"comparison_name": "surname",
"comparison_sort_order": 1,
"comparison_vector_value": 3,
"has_tf_adjustments": false,
"is_null_level": false,
"iteration": 3,
"label_for_charts": "Exact match",
"log2_bayes_factor": 9.966757404546238,
"m_probability": 0.7909844510739981,
"m_probability_description": "Amongst matching record comparisons, 79.10% of records are in the exact match comparison level",
"max_comparison_vector_value": 3,
"probability_two_random_records_match": 0.056726986814442375,
"sql_condition": "surname_l = surname_r",
"tf_adjustment_column": null,
"tf_adjustment_weight": 1,
"u_probability": 0.000790451099853488,
"u_probability_description": "Amongst non-matching record comparisons, 0.08% of records are in the exact match comparison level"
},
{
"bayes_factor": 392.6047733964612,
"bayes_factor_description": "If comparison level is `jaccard >= 0.9` then comparison is 392.60 times more likely to be a match",
"comparison_name": "surname",
"comparison_sort_order": 1,
"comparison_vector_value": 2,
"has_tf_adjustments": false,
"is_null_level": false,
"iteration": 3,
"label_for_charts": "jaccard >= 0.9",
"log2_bayes_factor": 8.61693390336724,
"m_probability": 0.027273861453286877,
"m_probability_description": "Amongst matching record comparisons, 2.73% of records are in the jaccard >= 0.9 comparison level",
"max_comparison_vector_value": 3,
"probability_two_random_records_match": 0.056726986814442375,
"sql_condition": "jaccard(surname_l, surname_r) >= 0.9",
"tf_adjustment_column": null,
"tf_adjustment_weight": 1,
"u_probability": 6.946900114672094e-05,
"u_probability_description": "Amongst non-matching record comparisons, 0.01% of records are in the jaccard >= 0.9 comparison level"
},
{
"bayes_factor": 3.3308445827934863,
"bayes_factor_description": "If comparison level is `jaccard >= 0.5` then comparison is 3.33 times more likely to be a match",
"comparison_name": "surname",
"comparison_sort_order": 1,
"comparison_vector_value": 1,
"has_tf_adjustments": false,
"is_null_level": false,
"iteration": 3,
"label_for_charts": "jaccard >= 0.5",
"log2_bayes_factor": 1.7358880394328378,
"m_probability": 0.1272719763293384,
"m_probability_description": "Amongst matching record comparisons, 12.73% of records are in the jaccard >= 0.5 comparison level",
"max_comparison_vector_value": 3,
"probability_two_random_records_match": 0.056726986814442375,
"sql_condition": "jaccard(surname_l, surname_r) >= 0.5",
"tf_adjustment_column": null,
"tf_adjustment_weight": 1,
"u_probability": 0.038210121536982354,
"u_probability_description": "Amongst non-matching record comparisons, 3.82% of records are in the jaccard >= 0.5 comparison level"
},
{
"bayes_factor": 0.05668437191545441,
"bayes_factor_description": "If comparison level is `all other comparisons` then comparison is 17.64 times less likely to be a match",
"comparison_name": "surname",
"comparison_sort_order": 1,
"comparison_vector_value": 0,
"has_tf_adjustments": false,
"is_null_level": false,
"iteration": 3,
"label_for_charts": "All other comparisons",
"log2_bayes_factor": -4.1409051559806,
"m_probability": 0.05446971114449472,
"m_probability_description": "Amongst matching record comparisons, 5.45% of records are in the all other comparisons comparison level",
"max_comparison_vector_value": 3,
"probability_two_random_records_match": 0.056726986814442375,
"sql_condition": "ELSE",
"tf_adjustment_column": null,
"tf_adjustment_weight": 1,
"u_probability": 0.9609299583620174,
"u_probability_description": "Amongst non-matching record comparisons, 96.09% of records are in the all other comparisons comparison level"
},
{
"bayes_factor": 4493.944951833532,
"bayes_factor_description": "If comparison level is `exact match` then comparison is 4,493.94 times more likely to be a match",
"comparison_name": "postcode_fake",
"comparison_sort_order": 2,
"comparison_vector_value": 2,
"has_tf_adjustments": false,
"is_null_level": false,
"iteration": 3,
"label_for_charts": "Exact match",
"log2_bayes_factor": 12.133766737127575,
"m_probability": 0.6945894628850235,
"m_probability_description": "Amongst matching record comparisons, 69.46% of records are in the exact match comparison level",
"max_comparison_vector_value": 2,
"probability_two_random_records_match": 0.056726986814442375,
"sql_condition": "postcode_fake_l = postcode_fake_r",
"tf_adjustment_column": null,
"tf_adjustment_weight": 1,
"u_probability": 0.00015456118629170805,
"u_probability_description": "Amongst non-matching record comparisons, 0.02% of records are in the exact match comparison level"
},
{
"bayes_factor": 235.38654463613585,
"bayes_factor_description": "If comparison level is `levenshtein <= 2` then comparison is 235.39 times more likely to be a match",
"comparison_name": "postcode_fake",
"comparison_sort_order": 2,
"comparison_vector_value": 1,
"has_tf_adjustments": false,
"is_null_level": false,
"iteration": 3,
"label_for_charts": "levenshtein <= 2",
"log2_bayes_factor": 7.878888043959358,
"m_probability": 0.144440475690058,
"m_probability_description": "Amongst matching record comparisons, 14.44% of records are in the levenshtein <= 2 comparison level",
"max_comparison_vector_value": 2,
"probability_two_random_records_match": 0.056726986814442375,
"sql_condition": "levenshtein(postcode_fake_l, postcode_fake_r) <= 2",
"tf_adjustment_column": null,
"tf_adjustment_weight": 1,
"u_probability": 0.0006136309784118558,
"u_probability_description": "Amongst non-matching record comparisons, 0.06% of records are in the levenshtein <= 2 comparison level"
},
{
"bayes_factor": 0.16109381243074802,
"bayes_factor_description": "If comparison level is `all other comparisons` then comparison is 6.21 times less likely to be a match",
"comparison_name": "postcode_fake",
"comparison_sort_order": 2,
"comparison_vector_value": 0,
"has_tf_adjustments": false,
"is_null_level": false,
"iteration": 3,
"label_for_charts": "All other comparisons",
"log2_bayes_factor": -2.6340270133004244,
"m_probability": 0.1609700614262565,
"m_probability_description": "Amongst matching record comparisons, 16.10% of records are in the all other comparisons comparison level",
"max_comparison_vector_value": 2,
"probability_two_random_records_match": 0.056726986814442375,
"sql_condition": "ELSE",
"tf_adjustment_column": null,
"tf_adjustment_weight": 1,
"u_probability": 0.9992318078352964,
"u_probability_description": "Amongst non-matching record comparisons, 99.92% of records are in the all other comparisons comparison level"
},
{
"bayes_factor": 157.6589769199383,
"bayes_factor_description": "If comparison level is `exact match` then comparison is 157.66 times more likely to be a match",
"comparison_name": "birth_place",
"comparison_sort_order": 3,
"comparison_vector_value": 1,
"has_tf_adjustments": false,
"is_null_level": false,
"iteration": 3,
"label_for_charts": "Exact match",
"log2_bayes_factor": 7.300663507522989,
"m_probability": 0.8498386844837121,
"m_probability_description": "Amongst matching record comparisons, 84.98% of records are in the exact match comparison level",
"max_comparison_vector_value": 1,
"probability_two_random_records_match": 0.056726986814442375,
"sql_condition": "birth_place_l = birth_place_r",
"tf_adjustment_column": null,
"tf_adjustment_weight": 1,
"u_probability": 0.005390360264200329,
"u_probability_description": "Amongst non-matching record comparisons, 0.54% of records are in the exact match comparison level"
},
{
"bayes_factor": 0.15097512583634987,
"bayes_factor_description": "If comparison level is `all other comparisons` then comparison is 6.62 times less likely to be a match",
"comparison_name": "birth_place",
"comparison_sort_order": 3,
"comparison_vector_value": 0,
"has_tf_adjustments": false,
"is_null_level": false,
"iteration": 3,
"label_for_charts": "All other comparisons",
"log2_bayes_factor": -2.7276172194336814,
"m_probability": 0.15016131551715894,
"m_probability_description": "Amongst matching record comparisons, 15.02% of records are in the all other comparisons comparison level",
"max_comparison_vector_value": 1,
"probability_two_random_records_match": 0.056726986814442375,
"sql_condition": "ELSE",
"tf_adjustment_column": null,
"tf_adjustment_weight": 1,
"u_probability": 0.9946096397357996,
"u_probability_description": "Amongst non-matching record comparisons, 99.46% of records are in the all other comparisons comparison level"
},
{
"bayes_factor": 25.776533349576276,
"bayes_factor_description": "If comparison level is `exact match` then comparison is 25.78 times more likely to be a match",
"comparison_name": "occupation",
"comparison_sort_order": 4,
"comparison_vector_value": 1,
"has_tf_adjustments": false,
"is_null_level": false,
"iteration": 3,
"label_for_charts": "Exact match",
"log2_bayes_factor": 4.687986345566368,
"m_probability": 0.8972366603544166,
"m_probability_description": "Amongst matching record comparisons, 89.72% of records are in the exact match comparison level",
"max_comparison_vector_value": 1,
"probability_two_random_records_match": 0.056726986814442375,
"sql_condition": "occupation_l = occupation_r",
"tf_adjustment_column": null,
"tf_adjustment_weight": 1,
"u_probability": 0.03480827496026209,
"u_probability_description": "Amongst non-matching record comparisons, 3.48% of records are in the exact match comparison level"
},
{
"bayes_factor": 0.1064693542015801,
"bayes_factor_description": "If comparison level is `all other comparisons` then comparison is 9.39 times less likely to be a match",
"comparison_name": "occupation",
"comparison_sort_order": 4,
"comparison_vector_value": 0,
"has_tf_adjustments": false,
"is_null_level": false,
"iteration": 3,
"label_for_charts": "All other comparisons",
"log2_bayes_factor": -3.2314898654108246,
"m_probability": 0.10276333964568997,
"m_probability_description": "Amongst matching record comparisons, 10.28% of records are in the all other comparisons comparison level",
"max_comparison_vector_value": 1,
"probability_two_random_records_match": 0.056726986814442375,
"sql_condition": "ELSE",
"tf_adjustment_column": null,
"tf_adjustment_weight": 1,
"u_probability": 0.9651917250397379,
"u_probability_description": "Amongst non-matching record comparisons, 96.52% of records are in the all other comparisons comparison level"
},
{
"bayes_factor": 0.06013846046847862,
"bayes_factor_description": "The probability that two random records drawn at random match is 0.057 or one in 17.6 records.This is equivalent to a starting match weight of -4.056.",
"comparison_name": "probability_two_random_records_match",
"comparison_sort_order": -1,
"comparison_vector_value": 0,
"has_tf_adjustments": false,
"is_null_level": false,
"iteration": 4,
"label_for_charts": "",
"log2_bayes_factor": -4.055568254069667,
"m_probability": null,
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"max_comparison_vector_value": 0,
"probability_two_random_records_match": 0.056726986814442375,
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"tf_adjustment_weight": null,
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},
{
"bayes_factor": 42.57918965897765,
"bayes_factor_description": "If comparison level is `exact match` then comparison is 42.58 times more likely to be a match",
"comparison_name": "first_name",
"comparison_sort_order": 0,
"comparison_vector_value": 3,
"has_tf_adjustments": false,
"is_null_level": false,
"iteration": 4,
"label_for_charts": "Exact match",
"log2_bayes_factor": 5.412076588451669,
"m_probability": 0.5558653370649939,
"m_probability_description": "Amongst matching record comparisons, 55.59% of records are in the exact match comparison level",
"max_comparison_vector_value": 3,
"probability_two_random_records_match": 0.056726986814442375,
"sql_condition": "first_name_l = first_name_r",
"tf_adjustment_column": null,
"tf_adjustment_weight": 1,
"u_probability": 0.01305485946343725,
"u_probability_description": "Amongst non-matching record comparisons, 1.31% of records are in the exact match comparison level"
},
{
"bayes_factor": 17.63445713473013,
"bayes_factor_description": "If comparison level is `jaccard >= 0.9` then comparison is 17.63 times more likely to be a match",
"comparison_name": "first_name",
"comparison_sort_order": 0,
"comparison_vector_value": 2,
"has_tf_adjustments": false,
"is_null_level": false,
"iteration": 4,
"label_for_charts": "jaccard >= 0.9",
"log2_bayes_factor": 4.140325258908541,
"m_probability": 0.007430809366225286,
"m_probability_description": "Amongst matching record comparisons, 0.74% of records are in the jaccard >= 0.9 comparison level",
"max_comparison_vector_value": 3,
"probability_two_random_records_match": 0.056726986814442375,
"sql_condition": "jaccard(first_name_l, first_name_r) >= 0.9",
"tf_adjustment_column": null,
"tf_adjustment_weight": 1,
"u_probability": 0.00042138010313857066,
"u_probability_description": "Amongst non-matching record comparisons, 0.04% of records are in the jaccard >= 0.9 comparison level"
},
{
"bayes_factor": 6.364203448564137,
"bayes_factor_description": "If comparison level is `jaccard >= 0.5` then comparison is 6.36 times more likely to be a match",
"comparison_name": "first_name",
"comparison_sort_order": 0,
"comparison_vector_value": 1,
"has_tf_adjustments": false,
"is_null_level": false,
"iteration": 4,
"label_for_charts": "jaccard >= 0.5",
"log2_bayes_factor": 2.6699799559617055,
"m_probability": 0.2268211159258972,
"m_probability_description": "Amongst matching record comparisons, 22.68% of records are in the jaccard >= 0.5 comparison level",
"max_comparison_vector_value": 3,
"probability_two_random_records_match": 0.056726986814442375,
"sql_condition": "jaccard(first_name_l, first_name_r) >= 0.5",
"tf_adjustment_column": null,
"tf_adjustment_weight": 1,
"u_probability": 0.035640142204610314,
"u_probability_description": "Amongst non-matching record comparisons, 3.56% of records are in the jaccard >= 0.5 comparison level"
},
{
"bayes_factor": 0.22072389682447663,
"bayes_factor_description": "If comparison level is `all other comparisons` then comparison is 4.53 times less likely to be a match",
"comparison_name": "first_name",
"comparison_sort_order": 0,
"comparison_vector_value": 0,
"has_tf_adjustments": false,
"is_null_level": false,
"iteration": 4,
"label_for_charts": "All other comparisons",
"log2_bayes_factor": -2.1796852624432685,
"m_probability": 0.20988273764202173,
"m_probability_description": "Amongst matching record comparisons, 20.99% of records are in the all other comparisons comparison level",
"max_comparison_vector_value": 3,
"probability_two_random_records_match": 0.056726986814442375,
"sql_condition": "ELSE",
"tf_adjustment_column": null,
"tf_adjustment_weight": 1,
"u_probability": 0.9508836182288138,
"u_probability_description": "Amongst non-matching record comparisons, 95.09% of records are in the all other comparisons comparison level"
},
{
"bayes_factor": 996.9629081638653,
"bayes_factor_description": "If comparison level is `exact match` then comparison is 996.96 times more likely to be a match",
"comparison_name": "surname",
"comparison_sort_order": 1,
"comparison_vector_value": 3,
"has_tf_adjustments": false,
"is_null_level": false,
"iteration": 4,
"label_for_charts": "Exact match",
"log2_bayes_factor": 9.961396020172025,
"m_probability": 0.7880504272712593,
"m_probability_description": "Amongst matching record comparisons, 78.81% of records are in the exact match comparison level",
"max_comparison_vector_value": 3,
"probability_two_random_records_match": 0.056726986814442375,
"sql_condition": "surname_l = surname_r",
"tf_adjustment_column": null,
"tf_adjustment_weight": 1,
"u_probability": 0.000790451099853488,
"u_probability_description": "Amongst non-matching record comparisons, 0.08% of records are in the exact match comparison level"
},
{
"bayes_factor": 391.4377585053876,
"bayes_factor_description": "If comparison level is `jaccard >= 0.9` then comparison is 391.44 times more likely to be a match",
"comparison_name": "surname",
"comparison_sort_order": 1,
"comparison_vector_value": 2,
"has_tf_adjustments": false,
"is_null_level": false,
"iteration": 4,
"label_for_charts": "jaccard >= 0.9",
"log2_bayes_factor": 8.61263911636049,
"m_probability": 0.027192790094480643,
"m_probability_description": "Amongst matching record comparisons, 2.72% of records are in the jaccard >= 0.9 comparison level",
"max_comparison_vector_value": 3,
"probability_two_random_records_match": 0.056726986814442375,
"sql_condition": "jaccard(surname_l, surname_r) >= 0.9",
"tf_adjustment_column": null,
"tf_adjustment_weight": 1,
"u_probability": 6.946900114672094e-05,
"u_probability_description": "Amongst non-matching record comparisons, 0.01% of records are in the jaccard >= 0.9 comparison level"
},
{
"bayes_factor": 3.348792035246015,
"bayes_factor_description": "If comparison level is `jaccard >= 0.5` then comparison is 3.35 times more likely to be a match",
"comparison_name": "surname",
"comparison_sort_order": 1,
"comparison_vector_value": 1,
"has_tf_adjustments": false,
"is_null_level": false,
"iteration": 4,
"label_for_charts": "jaccard >= 0.5",
"log2_bayes_factor": 1.743640785410052,
"m_probability": 0.12795775066882872,
"m_probability_description": "Amongst matching record comparisons, 12.80% of records are in the jaccard >= 0.5 comparison level",
"max_comparison_vector_value": 3,
"probability_two_random_records_match": 0.056726986814442375,
"sql_condition": "jaccard(surname_l, surname_r) >= 0.5",
"tf_adjustment_column": null,
"tf_adjustment_weight": 1,
"u_probability": 0.038210121536982354,
"u_probability_description": "Amongst non-matching record comparisons, 3.82% of records are in the jaccard >= 0.5 comparison level"
},
{
"bayes_factor": 0.05910839959746899,
"bayes_factor_description": "If comparison level is `all other comparisons` then comparison is 16.92 times less likely to be a match",
"comparison_name": "surname",
"comparison_sort_order": 1,
"comparison_vector_value": 0,
"has_tf_adjustments": false,
"is_null_level": false,
"iteration": 4,
"label_for_charts": "All other comparisons",
"log2_bayes_factor": -4.0804930306654015,
"m_probability": 0.05679903196404137,
"m_probability_description": "Amongst matching record comparisons, 5.68% of records are in the all other comparisons comparison level",
"max_comparison_vector_value": 3,
"probability_two_random_records_match": 0.056726986814442375,
"sql_condition": "ELSE",
"tf_adjustment_column": null,
"tf_adjustment_weight": 1,
"u_probability": 0.9609299583620174,
"u_probability_description": "Amongst non-matching record comparisons, 96.09% of records are in the all other comparisons comparison level"
},
{
"bayes_factor": 4478.216310309263,
"bayes_factor_description": "If comparison level is `exact match` then comparison is 4,478.22 times more likely to be a match",
"comparison_name": "postcode_fake",
"comparison_sort_order": 2,
"comparison_vector_value": 2,
"has_tf_adjustments": false,
"is_null_level": false,
"iteration": 4,
"label_for_charts": "Exact match",
"log2_bayes_factor": 12.128708500720714,
"m_probability": 0.6921584253922755,
"m_probability_description": "Amongst matching record comparisons, 69.22% of records are in the exact match comparison level",
"max_comparison_vector_value": 2,
"probability_two_random_records_match": 0.056726986814442375,
"sql_condition": "postcode_fake_l = postcode_fake_r",
"tf_adjustment_column": null,
"tf_adjustment_weight": 1,
"u_probability": 0.00015456118629170805,
"u_probability_description": "Amongst non-matching record comparisons, 0.02% of records are in the exact match comparison level"
},
{
"bayes_factor": 234.9307554121316,
"bayes_factor_description": "If comparison level is `levenshtein <= 2` then comparison is 234.93 times more likely to be a match",
"comparison_name": "postcode_fake",
"comparison_sort_order": 2,
"comparison_vector_value": 1,
"has_tf_adjustments": false,
"is_null_level": false,
"iteration": 4,
"label_for_charts": "levenshtein <= 2",
"log2_bayes_factor": 7.876091782546265,
"m_probability": 0.1441607893025827,
"m_probability_description": "Amongst matching record comparisons, 14.42% of records are in the levenshtein <= 2 comparison level",
"max_comparison_vector_value": 2,
"probability_two_random_records_match": 0.056726986814442375,
"sql_condition": "levenshtein(postcode_fake_l, postcode_fake_r) <= 2",
"tf_adjustment_column": null,
"tf_adjustment_weight": 1,
"u_probability": 0.0006136309784118558,
"u_probability_description": "Amongst non-matching record comparisons, 0.06% of records are in the levenshtein <= 2 comparison level"
},
{
"bayes_factor": 0.1638066202666821,
"bayes_factor_description": "If comparison level is `all other comparisons` then comparison is 6.10 times less likely to be a match",
"comparison_name": "postcode_fake",
"comparison_sort_order": 2,
"comparison_vector_value": 0,
"has_tf_adjustments": false,
"is_null_level": false,
"iteration": 4,
"label_for_charts": "All other comparisons",
"log2_bayes_factor": -2.6099344300152416,
"m_probability": 0.16368078530446667,
"m_probability_description": "Amongst matching record comparisons, 16.37% of records are in the all other comparisons comparison level",
"max_comparison_vector_value": 2,
"probability_two_random_records_match": 0.056726986814442375,
"sql_condition": "ELSE",
"tf_adjustment_column": null,
"tf_adjustment_weight": 1,
"u_probability": 0.9992318078352964,
"u_probability_description": "Amongst non-matching record comparisons, 99.92% of records are in the all other comparisons comparison level"
},
{
"bayes_factor": 157.25209579829618,
"bayes_factor_description": "If comparison level is `exact match` then comparison is 157.25 times more likely to be a match",
"comparison_name": "birth_place",
"comparison_sort_order": 3,
"comparison_vector_value": 1,
"has_tf_adjustments": false,
"is_null_level": false,
"iteration": 4,
"label_for_charts": "Exact match",
"log2_bayes_factor": 7.296935434718144,
"m_probability": 0.8476454486533592,
"m_probability_description": "Amongst matching record comparisons, 84.76% of records are in the exact match comparison level",
"max_comparison_vector_value": 1,
"probability_two_random_records_match": 0.056726986814442375,
"sql_condition": "birth_place_l = birth_place_r",
"tf_adjustment_column": null,
"tf_adjustment_weight": 1,
"u_probability": 0.005390360264200329,
"u_probability_description": "Amongst non-matching record comparisons, 0.54% of records are in the exact match comparison level"
},
{
"bayes_factor": 0.15318024806780273,
"bayes_factor_description": "If comparison level is `all other comparisons` then comparison is 6.53 times less likely to be a match",
"comparison_name": "birth_place",
"comparison_sort_order": 3,
"comparison_vector_value": 0,
"has_tf_adjustments": false,
"is_null_level": false,
"iteration": 4,
"label_for_charts": "All other comparisons",
"log2_bayes_factor": -2.70669781493369,
"m_probability": 0.1523545513453577,
"m_probability_description": "Amongst matching record comparisons, 15.24% of records are in the all other comparisons comparison level",
"max_comparison_vector_value": 1,
"probability_two_random_records_match": 0.056726986814442375,
"sql_condition": "ELSE",
"tf_adjustment_column": null,
"tf_adjustment_weight": 1,
"u_probability": 0.9946096397357996,
"u_probability_description": "Amongst non-matching record comparisons, 99.46% of records are in the all other comparisons comparison level"
},
{
"bayes_factor": 25.76308302430687,
"bayes_factor_description": "If comparison level is `exact match` then comparison is 25.76 times more likely to be a match",
"comparison_name": "occupation",
"comparison_sort_order": 4,
"comparison_vector_value": 1,
"has_tf_adjustments": false,
"is_null_level": false,
"iteration": 4,
"label_for_charts": "Exact match",
"log2_bayes_factor": 4.6872333435314175,
"m_probability": 0.8967684777341342,
"m_probability_description": "Amongst matching record comparisons, 89.68% of records are in the exact match comparison level",
"max_comparison_vector_value": 1,
"probability_two_random_records_match": 0.056726986814442375,
"sql_condition": "occupation_l = occupation_r",
"tf_adjustment_column": null,
"tf_adjustment_weight": 1,
"u_probability": 0.03480827496026209,
"u_probability_description": "Amongst non-matching record comparisons, 3.48% of records are in the exact match comparison level"
},
{
"bayes_factor": 0.10695442116596324,
"bayes_factor_description": "If comparison level is `all other comparisons` then comparison is 9.35 times less likely to be a match",
"comparison_name": "occupation",
"comparison_sort_order": 4,
"comparison_vector_value": 0,
"has_tf_adjustments": false,
"is_null_level": false,
"iteration": 4,
"label_for_charts": "All other comparisons",
"log2_bayes_factor": -3.2249319745875216,
"m_probability": 0.10323152226580272,
"m_probability_description": "Amongst matching record comparisons, 10.32% of records are in the all other comparisons comparison level",
"max_comparison_vector_value": 1,
"probability_two_random_records_match": 0.056726986814442375,
"sql_condition": "ELSE",
"tf_adjustment_column": null,
"tf_adjustment_weight": 1,
"u_probability": 0.9651917250397379,
"u_probability_description": "Amongst non-matching record comparisons, 96.52% of records are in the all other comparisons comparison level"
}
]
},
"params": [
{
"bind": {
"input": "range",
"max": 4,
"min": 0,
"step": 1
},
"description": "Filter by the interation number",
"name": "iteration_number",
"value": 4
}
],
"resolve": {
"axis": {
"y": "independent"
},
"scale": {
"y": "independent"
}
},
"selection": {
"zoom_selector": {
"bind": "scales",
"encodings": [
"x"
],
"type": "interval"
}
},
"title": {
"subtitle": "Training session blocked on l.dob = r.dob",
"text": "Model parameters (components of final match weight)"
},
"transform": [
{
"filter": "(datum.iteration == iteration_number)"
}
],
"vconcat": [
{
"encoding": {
"color": {
"field": "log2_bayes_factor",
"scale": {
"domain": [
-10,
0,
10
],
"range": [
"red",
"orange",
"green"
]
},
"title": "Match weight",
"type": "quantitative"
},
"tooltip": [
{
"field": "comparison_name",
"title": "Comparison name",
"type": "nominal"
},
{
"field": "probability_two_random_records_match",
"format": ".4f",
"title": "Probability two random records match",
"type": "nominal"
},
{
"field": "log2_bayes_factor",
"format": ",.4f",
"title": "Equivalent match weight",
"type": "quantitative"
},
{
"field": "bayes_factor_description",
"title": "Match weight description",
"type": "nominal"
}
],
"x": {
"axis": {
"domain": false,
"labels": false,
"ticks": false,
"title": ""
},
"field": "log2_bayes_factor",
"scale": {
"domain": [
-10,
10
]
},
"type": "quantitative"
},
"y": {
"axis": {
"title": "Prior (starting) match weight",
"titleAlign": "right",
"titleAngle": 0,
"titleFontWeight": "normal"
},
"field": "label_for_charts",
"sort": {
"field": "comparison_vector_value",
"order": "descending"
},
"type": "nominal"
}
},
"height": 30,
"mark": {
"clip": true,
"height": 20,
"type": "bar"
},
"selection": {
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"encodings": [
"x"
],
"type": "interval"
}
},
"transform": [
{
"filter": "(datum.comparison_name == 'probability_two_random_records_match')"
}
]
},
{
"encoding": {
"color": {
"field": "log2_bayes_factor",
"scale": {
"domain": [
-10,
0,
10
],
"range": [
"red",
"orange",
"green"
]
},
"title": "Match weight",
"type": "quantitative"
},
"row": {
"field": "comparison_name",
"header": {
"labelAlign": "left",
"labelAnchor": "middle",
"labelAngle": 0
},
"sort": {
"field": "comparison_sort_order"
},
"type": "nominal"
},
"tooltip": [
{
"field": "comparison_name",
"title": "Comparison name",
"type": "nominal"
},
{
"field": "label_for_charts",
"title": "Label",
"type": "ordinal"
},
{
"field": "sql_condition",
"title": "SQL condition",
"type": "nominal"
},
{
"field": "m_probability",
"format": ".4f",
"title": "M probability",
"type": "quantitative"
},
{
"field": "u_probability",
"format": ".4f",
"title": "U probability",
"type": "quantitative"
},
{
"field": "bayes_factor",
"format": ",.4f",
"title": "Bayes factor = m/u",
"type": "quantitative"
},
{
"field": "log2_bayes_factor",
"format": ",.4f",
"title": "Match weight = log2(m/u)",
"type": "quantitative"
},
{
"field": "bayes_factor_description",
"title": "Match weight description",
"type": "nominal"
}
],
"x": {
"axis": {
"title": "Comparison level match weight = log2(m/u)"
},
"field": "log2_bayes_factor",
"scale": {
"domain": [
-10,
10
]
},
"type": "quantitative"
},
"y": {
"axis": {
"title": null
},
"field": "label_for_charts",
"sort": {
"field": "comparison_vector_value",
"order": "descending"
},
"type": "nominal"
}
},
"mark": {
"clip": true,
"type": "bar"
},
"resolve": {
"axis": {
"y": "independent"
},
"scale": {
"y": "independent"
}
},
"selection": {
"zoom_selector": {
"bind": "scales",
"encodings": [
"x"
],
"type": "interval"
}
},
"transform": [
{
"filter": "(datum.comparison_name != 'probability_two_random_records_match')"
}
]
}
]
},
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BKmOJRUqcGw/n+PHjTVcK4xxQzIQHlTFT1HkYz9yneMFZ+EQoDcdTiC9mPALxfrur4gAqbRDywIsThZcxwlv8tnZ459E6NmQE29Nn7hViinnZjAVU+oKt0Y0XYh+fnCqg8vIWe728XPswIwCZ31WkQD4pIEDNJ2uprxsBKnF5xGR6aOHBD2wCRn4fVDwNACwxeb7wdxZ74AVhcQ0PG+9ZYnrWx30SX8ZDiCk8P10J6OKZ4N+i9kH1Dwcg0HtW8NYAYkznMcWLRwdg5HP2uuQBBmzyoGJqLhZQ8ZrxObBGIZYOCKJ9Yt9SBVQgAQ8wMXEe7mjPe2N5aMYuHMPrB9Qk08B7MGM9qMTfAUh+gRPt48XBM0h8bGzBWwps8nDHZnj7+H9s3/BG+v1tsTltoxuFBzgQxIsGEAa8e938Q9tP8X/44YcGjfExqEA4wEIhTAH7AjObGjseUD2QeK8XY4hrBQQZa8Ck7yuARv82VZeXGECFcQfQoA828IvpaI8xyGI5H+vqdSU2F6hlBsEX4IXrAth8/CLtEw8bX4itZHyhOdoni0FNdt2EvlDP68nLA8DIuQiNSbYPanHtV9R5GAdAIveen3L3YRP+ehmTvLzyPRAPqP6FMDZEJ1Ynv58rNsBWPjSIl53FixdbyBEFHWK30yKenDHPjiE+7IZwEY7nO4OXYgqeb76jfDiAXyQF2DIefv75Z5v98X//448/Nrpe//2UKFxGjxMpEHUFBKhRt5D6lzEFWOAEwLJQiYdDbCE+FfADSuL/xnFMz+OdY5qUmNZUsuB4QCXekIc9XlsWZPgHF+3yUCHMgJjBVNoELOkHi4F8uECIQGjCwxRoiY8NZQqb6+YhSN/S0YBrZqqchVF+mje+v0Ag18NLBsDjC2AM2KCNX+Dl/+b7Auzi4Y6H3lQ18TGoQBO2Z+o2UeadosbOps6FzdCBtrmWTe13Gdse1wmIYBtvH/TyY5WxGDue4vvCORkvhK7kOqMQ/cTDne3zFnUetOfvaA4MUphxQT+2evMe1U3ZMPTvnJMXYc6ZLGsXMyx8F7BokZduQhh4cSb0h1mFVErs9XI8L77swACgqkiBfFNAgJpvFlN/80aBWEDNVUamvBEnrqPes4WHDa9erkosoBIvqiIFSkoBdu/Ak0+J3bIs3exqeFqJW2dGiZhkFSmQbwoIUPPNYupv3ijAVlVMU7OTQKIN6vPmQnLQUTzJxMnhOSLsIVeFKWymZ5muxbulIgVKUgFiVnlJIxSFmQG8p+nuX8oLMrHshAOoSIF8VECAmo9WU5+lgBSQAlJACkgBKVDACghQC9i4ujQpIAWkgBSQAlJACuSjAgLUfLSa+iwFpEBGFWDVOyXdxVYZ7UxcYyyUYiFUUYuhsnl+2qYPm1rcxTHol8piv0z3NxWNomzjTOuh9qRAISggQC0EK+oaClIBtilKFn/m0x2mcuEsviADFRuEJyt+43FWs+fLYiG2EWKz+0wsQGNfTvKrk6YyvrB9FbswxGcTSkV7YlwBI/bjTbewVRCLudj7NJOFvVHZZqmoccH5SBHLFmg+I1myPrBDAXvW5nKRm+9LKhoVZeNM6qq2pIAUyIwCAtTM6KhWpEDGFWCLHrbIoZBhhv0v2ayeQl73ZNvVxHeENtiPk70/kxW22QJYGjRosNF2Uxm/sAw1yB6nQGUmFjdtClBJLgAMF7ewlybePbL/pFtSga902mYssQiH7cSKKgLUdNRVHSkgBUIVEKCGKqj6UiAHCrB5N94pVpxT8MqxkT9gBfywSTgb17MpOODBBuVkyiKrEuk7+ZykA6xWZy9Y9mRkg3fSLbJROXtEssUNuw6w9yhb1LCinnOyETjpKwFczktbZAgirSjnYVoXEIstQDEb/7OTAXtNkqecLFEUsgqxrRQJA9iMnNX7TB+Tu5y2SVZAv0iPSgarZJ/jUSQLFZBF8gO8lWRbwoNHFib2gEx2vT5Fpu8zOrLXKZnC5syZY32lDfaAxYPqATXZeYBQsmyxsTrXS3tdunQxXTygsmE8yRHYQP/YY49NqgP7npIogXSxhx12mO2h2rZt2408qBzHDhHYnuvhXBdffLHtJZvMfrE2KgpQWU1Ozndsi54kCcCDiv35HF3ZeQHPPJmY2B8YDyq2IFMSfSODFVnO0g2bYP9gxinjlfERmy2rKI1IQsC52fuTvXUZaySzKMrGObiFdQopIAWKqYAAtZiC6XApUBIKxAMq0MNG/WRRYq/DK6+80nLBkwUJSAEMAT28X2xb46f4mcJm+xkgiQw4QB4/tMFm+GT7WbRokcEqGW940AOaxD+ykTjZuGiL7Dqffvqp5Rtn+pu88LEFaCa0AHghuxKgQoYo9mQEKknRCeCSEYr+0g7ATTpKzsv1AtxsUJ7sc6b4mXYnZSww3bVrVwuJIHsSAAOA0fdE18v5YgvwAhBzLNv7kJqWVJEApwdUYDHZefBwX3PNNY4MTMA52aPYDB6owlZcIzDPMQA5tkmmA31BJ44jExdaA8vxU/zYgZcP0niyETx2AA6xVTL7pQKo2J+EEsAvLwr0mWxWACovLGgDpDLtTx8IsUBzAJWkDNgW0OeFAT1iw1RoY/r06QlvoYMPPniDhAyMHcYNWcB42YrdyzOZRni5CVGhb6QWRY977rnHkYWNsZ/MxiVxT+ucUkAKFK2AAFUjRArkgQLJABUvk4/BBEqBHhapkMcbSCJMgKn7WEB97bXXDDYpPMQBEsAjHlABRPZRBDBJ3clUMGBAZhq/tyKZbipUqLARoAJTpEfFOwvUkcKSzceBFTyyw4YNs/OT8hHvHCEIAA6xlnhRyZrEZ0B3ss89oBI3yzV58AGcgV+uC7hLdL3xQM114Tn1bbA5Oh5JrtkDKsCZ7Dx4ONHCgy8gxAsA2YBIoUvOeqbqgTfKKaecklAH+k7YAvbgGOB2xx13NCCMBVT6he4AGN5YCjbGXu3atTNATWS/VACVMcV4w3NNAY7x6AKX2ANg9ClUsSMvO3iusRN98DnfGTOEosSmZsXrmix7E+OUlKS+AKjoFZs2lL/Rj2Qa0Qfis9GHlzcKswO8KDDmk9k4D74C1EUpsNkpIEDd7EyuC85HBZIBKvADjFGAKmABbxepSwGqRIBKOkUghAIcAk9M2ccCauyiKqaamSIFDEhXClQylUsBIuhDPPAxLQ2MAqb0pVu3bjblzZQ9XtrYAkgAFHh68QLSf7yNeLw4T7LPPaAC0OSiHzBggDXLNQO4eBeZkk90vYQ7xBYAFXDy4OVzwgOIACBT/C+99FLS8+DdA4aBw9ji87nzGWED9IeSTIeZM2fatDRQBoRRfBhDLKAS6lG7dm1HOlgfi4xexOXi+Uxmv9i+JZvi58WGdL/kfqcwPgjvwP7AHsDZqlUr+xu2IQkFfwMO8Th7YAaqp06dai846ZRkY4sXl2QaEQoA1PpQGM7LOOEFgnrJbJxuGEI616U6UkAKpKaAADU1nXSUFChRBZIBKvF2PKzJt87UJh5LPFdAIFOmiQCVmD7vwUwGqEy94xWjxAIqEMIUL1P/FDxrK1as2AhQCS1gqpy4Try5QBMge+GFF9o0vvc0Mh3OdDCwxd/pM3WJeSW2kuvCQxr/OQAMFDLFz/XgGSNukkIWHjxxhBTgjU10vYkAlboAFoU4WUAcr5v3oPr41ETnwWPJinhCLSi0X61aNesTXlHAlz4BqICqD1uI16FmzZoGgcRP4nGmVK1a1by5sYDqd13Ay+m9jtidKXl+ktkvFUDlJQfQRX8KLx/ECgOhaB4bD4te2BcIZWwAlX5Knz4wBmLDKQB+pu0TFcYSLxq+JANUPMPJNGJqH0824wropM94mumf9+QmsnGJ3tw6uRSQAgkVEKBqYEiBPFBgU4DKwhhiMYExPGo87JkmJjYRwIyd4g8BVIAQAGQLLMAUYAG44j2op556qoETcZQABX2aNWuWgdsTTzxhU994Tjt16mQLbABCFvoQs4jXlPhVYkCZpt9zzz03+hxYxFNHf4BXYm9pE2+ZT18KDOJVTBVQuQb6SGwvegE6gKEHVIAx2XlYiIMX+qmnnjLNAW5eEvCq+kVShEU8+eST5snDu5hIB66fqX2m9WkTDyQxuYliUNEYjyALotCYkAJCEICz4gIqOo8fP95ePFjMxcIowBPgw6uN9xjYI54TnQgnYREa3lPsRPwvgMqOCsTXMkZatmxpYEjsri9owUKxRIWxQuxrMkBlzFWpUsUW/iXTiPHEWCMul7FBvwmtYKxyTySzcR58BaiLUmCzU0CAutmZXBecjwokA1S8c0wXAw+A4vPPP2+XBzCwIh+AAFaKAlSmlUeOHGlT/HgrFy5cmBRwmHomxpAYS2JJiUflx4cMeG0BJSDHxwHi3cNry+IlPI0sAqIwlY+XEW8j10goAO3i+WXv0EsvvTTp534f1PPOO88gCmjifAAvi7mIl0wEqFyv94L6/jLFj9eSv1GAa+AK4PL7oOL9TXYewhKIfQRw6QOeVLymAC5beHEtgCOwDahin2Q6EIvJtD7QiRaESODBjF8kRRgC5+Q4CkBGrCXapwKo/jw+vIKxgn3oP/YCtilM5wOrjDFeDIhBBWK9/VjIRj8BVDygfis0tMKjv6kN/pPdj3i/mSHwnk/szQ87QhSlEd53wkm4DrThJQHbFWXjfPxOUJ+lQKErIEAtdAvr+jYrBZiWx/sGbDLFzQ/xhJkqQBHtEU7AAhgWaOEtZBeA+AL4ALuAC/3xBdDBY7dmzRrHlHZs5iE8XUzf4w2MBZtkn8eek+2xABLOly4UoRcQBkgmy4hU1HnoO7C21VZbbVLyonRgCp/YWa4Fj26yQn8JgyBeNdV9cTfZsb8OwHZly5ZdHwvr67HVFNs/cY2EH8TrhK3Wrl1r8crZLEVpBGhjC+CeeOTYkoqNs9lvtS0FpEBqCghQU9NJR0kBKeCceSnZdggPIBDAlC8r31mpryIFpIAUkAJSIFMKCFAzpaTakQKbgQJMVxPryf6cbGJPLCBxiipSQApIASkgBTKpgAA1k2qqLSkgBaSAFJACUkAKSIFgBQSowRKqASkgBaSAFJACUkAKSIFMKiBAzaSaaksKSAEpIAWkgBSQAlIgWAEBarCEakAKSAEpIAWkgBSQAlIgkwoIUDOpptqKrALs1cjWRSpSQApIASkgBYqrAFuWqfxPAfaqprDvdLaKADVbyqrdSClASs9rr702Un2K7cxXX31lezZGtah/YZaRftIvTIGw2hp/0dHPp46mRyShIAnHsmXLbP9kColQyKqWrJCIgr2oSb6SrJCwhUx4L774omXcy0Yhgx97CpMZL1lhT2T2Z+Z6uK7YsnLlSsuQR3a+ww8/PGETAtRsWE5tRk4BAWqYSfSAk35hCoTV1viTfmEKhNXO5PiLBVQy5Q0ZMsQyAAKUlJtuusn17t07aYdJjrH//vu7KVOmJD2GNM9kgGNLQLLNZaMMGjTI0jiT/S9Z+fHHHy0dMxn1SO8cW0hEQkIUPufviYoANRuWU5uRU0CAGmaSTH5Bh/UkcW31L0xV6Sf9whQIq705jT8PqHhM8YTigcTDOGLECMt+5gEVcMXDuHz5ctesWTNL+Uy6aNI3k8aXlMl9+vQxwOU4vJmkSCZFMOmOAdRzzjnHkf2PQorgdu3arTfUd99950477TT76dmzp2vZsqWlZQZ8gU+8u+PHj7e00/fdd5977LHHLK02/ahXr561/ccff7ixY8daSuJLLrnErqVTp04GxqRbbt++vQEqkMx1kEGQtMP0kZTJL730kttjjz3cwIEDrR/xRYAadl+pdp4oIEANM9Tm9AAJU0oALf2yoUBYm7p/o6OfB1Sm9keNGmXT+61bt3Z77bWXQSGACtxVq1bNIO7kk092Xbp0sZ9TTz3VQG633XYzaKQOaaeBPJKm8Nntt99uKaQBVD7v2LGjGzBggKWBJgVxbBpovLGAMms0qlSpYiKRLhv4/PLLL+335s2bu4+6N+wzAAAgAElEQVQ//tg8pQ8++KD1d/Hixa5p06bu119/NSgFjCdPnuxOP/10N2fOHPf5559bSB1ZBwFUSq9evdwzzzxjbc2YMcPAmTa5nltuuSVhNkIB6l/jlrzgvIFUqFAhbCRnuDYucvqUbm7xkO7gvt9iiy2S5iQPaTuVutxMReUhT6UNf4wAtThqbXxsZB9wK79x7vZqYRen2u6rC+crBjpgHET2/vjrmtS/AOM65zKpnwdUvJSXX365GzdunGvbtq15Qi+77DIDVMAOT+asWbMMFB9//HGL05w2bZqLneK/6667zJMKENaoUcMgcdttt7U01ADq0KFD3cUXX2zAi4d21apV5n315YorrjBv6ejRo92VV15p3lIg9Mwzz7R2r776arf77ru7Jk2auGOOOcYyCL755pt2nuuvv94AdebMmcYogDAeXuCzcePGGwBqhw4dzAM7cuRId95551l9Fi3n7RQ/7mfoP7ZA9Rhv77333uDzp59+2gzxwgsvpD0KMRRvKyeeeGLCNhgslStXdgcddFDK5xg+fLi98QB61MV9XqpUqZTrcyC5zkuXLm0DOZOFAYxrHrd8soI7nwHFYMt04WbgLTBZ7MkPP/xgmv35558bnTodWwhQwyyYyS/osJ7E1RagZkROAWqYjJG9PwSoYYbNgn4eUB966CHjFso777xjMMrUOYzDdDvPXT+VTkwq7BEPqEAkkPr999/b85Spdpw6eCljY1A7d+5sAMxzP9YJByji/QRAWaQLoC5dutS8my+//LIrW7asO+KIIwyO8aT6csYZZxhoAqgAa6VKlVzXrl2tLx988IGrW7fuBoDqY1BhDuCXEIADDzww/wEVFzM5v1kNhvv40EMPNXd1bPnpp58cQLPffvulNRgxKm8wH330UVKAPP/8882IBDWnWjDaK6+8Yv2aPXu2a9iwYapV1x/HGw9vRvRtl112KXb9ZBV4K2Pw035JACqxKNxIflohvg9FAWo6thCghg2dyD6ABahhhvUPYHlQg3SM7P2RBcAKEipJ5c1Jv1hA/fTTT13//v3Nmwi0MWUPoPJcxPPJlD+QSOxorAd1++23t2l5dGvRooX9nTABYj4BRWAyFUBdvXq1K1++vFmFOFYAFe8thRll/s4qfEIGAOoxY8a4d9991z377LO2qMtP8XM+vKtM2b/33nsGrbFT/EUBKs/zO+64w84TXyI7xe89qMAngcQUXNG4qQkAxijEZhBki0EJ5iXQFoMTpEscBECLi5oVbxB/nTp13MMPP2wC+7gI2u3evbt56/iXqX4MjFeWcyD4ggULzH3Nfl+4qTkWwd944w132GGH2QDj7Sb2HHhi6S9vJXh22RKC83Is0/YYkMEF8NJ/tlzAS8q14cnlTQejMWBx+wOpvC3FFqCLlXC8sRAXwrWyeg+XO33B+8oAwjvM4MedzuBjMDdq1MiOYxCjHVtA3HnnnTZ9gA5MCeBB5fq4TgrB0WjuCxDJFha81RGCwJsW7XDtBHID5tw0BIAzhUCsC7/jVeZcDEgGJ4Od+BSmJmgDe+PZRWf0R3P6zjQHoB9rC2yUShGgpqJS8mMi+wARoIYZVoCaGf20TVyQjpH9fskC4McCKs86npHEl/Lc5XnPMxQuwIHEoingkFlYnuWwCAujiDMldhNgpD67AFDgELyiQCLPdn7HG/u3v/3N4lPjPajU8fGj8An8Q2wp7IS3k/Lcc8/ZOfHwUnh2wyL169c3iJ03b5778MMP3a233mrnY9so6rDwCiaBtfidhVFPPvmktQ2PEDLAVlV4e4Fj+ph3gAqUQPgAK0bhrQJ42XHHHe1CAZv58+e7+++/32I2gB+ChplCBtC4eMTwYuKCPuqoozaI6YT+GRyAFUHLQCygRz3aePXVVy1oGKgD9oA0jMg5gGJAD6CNPQfeVqAS+ANScb8zxe/hl7YZfAwOgPWRRx4xtzrXSB9w6WN43qzY94z28cbGFs7PAGVQ8HPDDTfYlAFeZmAcL+lTTz1l0wfElTCICKTmfPzOQKIPHMdgBkRZaXf00UfbajuugTc56j7xxBM2YIHh2ALE0j/e8v7v//7PMZXA/wFdbhr0R0M0Radzzz3X7MVnaMJAZwDTPv8C8tyYwLAHVMCdgY5duQlibZFqjKoANej5kdEYrLCexNUWoGZETk3xh8m4OQFWmFKJa0u/jXUhvA3uIQY0PjQQMIQnypUrZxWXLFlicMnCquKGEaZqT5xhPKP9OX09+oEzjthWnu84EeE0OKaovVqpT11mcWlzq622yj9A5S0BCAFSeVPAu8hqNADVx10AbwAqwcbEV/i3BLZA2HrrrW3FGfAJxTOVH1sYBNTh7QFPqw/w5Y0G7yCwhnix08qcH88tYIebncFCPQA19hx+ip+peR+DCqDSdzyMFI4BrHl7wLuKxxI3O+f0gMrf8RoS7hAPqFwjsSWcH28iHmcWNu2zzz4GvUDm1KlTDRQBRKYM6D8D2k/x88bG4L7tttuseVblAaYNGjQwUGaQ8Rke0fiYUNz4wCk68yWDB5j+ch2cA/vheQaGKQAm+57RFwCVc3Ddfk83/jZ37tz1gLpw4ULznhLPC/Bia03xp/qVkrnjIvsAEaBmxMgC1DAZI3t//HVZ6l9h2zfs6sJrMzPKs90XnHI43OCekJJXU/z+QoEwIM9vmeABFRc2Xkv/uQc9QAlPJV45vHyxxUMsoQF4AylMI+PhpF1gD/iLhSKfmYGpcmJj8VJ6QI09RzJA9dPoHlAJAWCfMzykPk6VGBM8n3hQAUzCFuIzNuBB9W0BfAQf452keEDF+8sUOVMGDBo8zPGAyrUBn37BEuBP3C+Bzn6RFDCOtvGACpAyOAFUvL/sd0ZIAl5a3qpw4QPCsfG32IDVfgAqUxxAPj+UBx54wGJZvAfVn4+3MrzaAtSQ2z39upF9wAlQ0zdqTE0BapiMkb0/BKhhhs0T/TJykYGN4EyCnXDIHXLIIRayF1oKClDxwrEyDI9gmzZtLKiX6enp06ebdzMRoCIgkIcHFi8rbwG4mnv06GEeQCAK2GKKGWC76KKLzJvLwiqgDkik/WSAytQ7LvpYD2oiQCXEAKOymS7pyQhA9h5UpsdZUQdoxpZUAJUpdNzt/AB36OIBFeAktoUYUOCS8xILi4bEuuAF3RSg4nn1i7d4cSD8Am8nwImeeEfZAgN90ZHwCtrkB22JOQVYGdwAPV5g9CkKUImPxRZcf6pFU/ypKpX4uMg+gAWoYYb1D2AtkgrSMbL3R54AlvQLGn4FWzmvAdXv0xm7zRTT/Hg2WZDDdgk+SBhAJe4RIIovQBXeU2AOLyWQxBQ77RPvSIAwoOszJOBdJWYSUOVYFgDh4WOKPPYcTI0TLIwnFOjzMajEiQC8Ho6BOaCY6XK8hwQ64x73e5vh2cRjDDTHA6pvi6BoFkbFelCBQMAPjybQxzUCvcTQErBMSAPwjAcXmHz//fdtOp5pfUIOOJ6QAGJvk3lQ6Q+xwBQ0ISaWlYQEdPMGhTeWuFQgHnv4rSiAS8IIuGbgmFhevNlsLYZWfBa7zVSsBzXWFqmmcROghn2HRfYBIkANM6wANTP6aZFUkI6R/X7JE8APEj/ClSMLqCGaAUcEF7MijqnnTRXiT/3KfwKMAVM8pCzKAdh8wbsIlDLtj3eVaXhAlq0YWMUefy6mp4mHZbp8UwVIZFqePhM2AJwBbPQHYGOqHphMp+AxxdNJgDPhD4Q+0HeA+bffflu/zQSxunhDs5EUAFClXV4cYgs7AQDlPjYYTzKe1GT7oyayRSqaCFBTUSn5MZF9gAhQwwwrQM2MfgLUIB0j+/0iQA2ya2jlggTUdEQhDpPUY6luW5TOOYqqg+eRLaCY2mcqn0VR7DjA53hS8WoWYuFlAABnyh4PK15c4ml9PHA618y2Vz7/cGx9vLVRLfqCTtMyAtQ0hduwmmJQw2TU/Sv9whRQ7UQKCFD/UgXvKD9MmZdUYaEWQcZkymJanQxSTK0zFR6/tUNJ9TEb58XjDVSyGwJbZOFBzXSRBzVM0cg+gAWoYYaVBzUz+smDGqRjZL9fsuBB3frm4mWTjBd2Te8Nsyvi5GG2NZYRmB1lpjQTC5WCDBtYWYAaKKCq54cCAtQwO0X2ASJADTOsADUz+glQg3SM7PdLFgC10q1hgPrDDf8F1HXr1lmGSRYlE3bIjCuFRdUsMiYkEHhlRjaTWSiDDF3MygLUYgqmw/NTAQFqmN0i+wARoIYZVoCaGf0EqEE6Rvb7JQuAWusfYYD6Wa//Aiozj4T+sUCaReAAKkDKvvF+L3gSC7FbDvuz52MRoOaj1dTnYisgQC22ZBtUiOwDRIAaZlgBamb0E6AG6RjZ75csAGqDvv8DVFCzKFxN9Pf3em44xU/mRkIBAVS2h2Qdjd/Nh73ISfuOlzUfiwA1H62mPhdbAQFqsSUToIZJlle1tUgqzFybE2CFKZW49uak3xH9Y5A0DUKd1iM5oJKFkW02fVIftokk0Q+JcfKxCFDz0Wrqc7EVEKAWWzIBaphkeVVbgBpmrs0JsMKUEqAec3vYFP9L1yQHVJ89k0VSbFHp41K7deuWDbNlvU0BatYl1gmioIAANcwKkX0Aa4o/zLB+ClOZpIJ0jOz94e2rEITI2PfkO8MA9bmrkwMqF0l6cdK+16lTxx133HHupptucj49e5AIJVBZgFoCouuUuVdAgBqmeWQfwALUMMMKUDOjnwAwSMfIfr9kAfDb3hUGqOO6bQyoxJwOGDDAejthwgTb051ChsjHH3/cvKn5WASo+Wg19bnYCghQiy3ZBhUi+wARoIYZVoCaGf0EqEE6Rvb7JQuAetbdYbD4WNcNATWR8GSIZF93VvDncxGg5rP11PeUFRCgpixVwgM3pwdImFKJa0u/MFWln/QLUyCsdibH34UDwwD1oSs2DahhVxud2gLU6NhCPcmiAgLUMHEz+QUd1hMBoPTLhgJhber+kH6pKnDZ4DBAHdJFgJqq1jpOCuSFAgLUMDPpASz9whQIq63xJ/3CFAirncnx1/XesH1Q7/6bADXMmqotBSKmgAA1zCCZ/IIO64k8qNIvGwqEtan7Q/qlqsC1Q8L2Qf3nZQLUVLXWcVIgLxQQoIaZSQ9g6RemQFhtjT/pF6ZAWO1Mjr8b7w+b4r/pUgFqmDVVWwpETIGJEye6Aw44IGK9UnekgBSQAlIgHxSoXr16Rrr5jwfCALXXJQLUjBhCjUgBKSAFpIAUkAJSQAr8V4E7h4YB6tWdBKgaS1JACkgBKSAFpIAUkAIZVGDwsDBA7XKRADWD5lBTUkAKSAEpIAWkgBSQAg8ODwPUizsKUDWKpIAUkAJSQApIASkgBTKowMiHwgD13AsFqBk0h5qSAlJACkgBKSAFpIAUGPNwGKCeeYEAVaNICkgBKSAFpIAUkAJSIIMKjB8RBqinnS9AzaA51JQUkAJSQApIASkgBaTApEfCAPWE8wSoGkVSQApIASkgBaSAFJACGVTglZFhgHr0uQLUDJpDTUkBKSAFpIAUkAJSQApMezQMUI84R4CqUSQFpIAUkAJSQApIASmQQQVmBgJqQwFqBq2hpqSAFJACUkAKSAEpIAXc3FFhHtSDzpYHVcNICkgBKSAFpIAUkAJSIIMKfBYIqLUEqBm0hpqSAlJACkgBKSAFpIAUcAsf+58HFV9oUf7URH/f66wNPairV69222yzjStdunTBqVvqzz//3Hz8xQVnPl2QFJACUkAKSAEpkC8KLA4E1N3+AtTly5e7Dh06uC233NItWrTIXXPNNe7888/PFxlS6qcANSWZdJAUkAJSQApIASkgBcIUWBEDqOm0tONfgNqvXz/3yy+/uNtuu80tWbLE7bbbbg5varly5dJpNpJ1BKiRNIs6JQWkgBSQAlJAChSaAr89HrZIqlyH/056X3TRRa5FixbuzDPPdEyEM8U/f/58V7169YKRTIBaMKbUhUgBKSAFpIAUkAJRVmBdIKBu8Regnn766Y6ftm3b2uVWqlTJvfvuu26vvfaK8uUXq28C1GLJpYOlgBSQAlJACkgBKZCmAoGr+N1fq/hvvvlmt/3227uuXbu6devWuYoVK7qff/65oBZLCVDTHGOqJgWkgBSQAlJACkiBYinwSNgUvzvvv1P8EyZMcIMHD3ZTpkxxTz75pBswYICbPn16sboS9YMFqFG3kPonBaSAFJACUkAKFIYCDwUC6oX/BdR//etf7oQTTnAff/yx/f7SSy+5xo0bF4ZGf12FALWgzKmLkQJSQApIASkgBSKrwNBAQO204c6g33zzjatcubIrU6ZMZC853Y4JUNNVTvXySoGJEye6Aw44IK/6rM5KgVwpsPSnd3N1Kp0nAwqUcqXcLhUbZaAlNZGqAhlbHX9/IKBeuvlsXS9ATXV06ri8VuCf//ynu/baayN7DV999VWktwdR/8KGTtT16/tA4EMzTB7VLqYCZ58y1VXdrWnKtaI+/jar/g0OvNe6CFBTHvg6UArkgwIC1DArbVYPkDCpEtaOun4C1CwYPYtNClCzKG6CpjN6/w4MBNQrBKi5tb7OllMF5s6d60iTFluIYcmnKfBvv/3Wsmjsv//+KWknQE1JpqQHZfQLOqwreQmAUddPgJqFQZnFJgWoWRQ324A6IBBQrxKg5tb6OltOFWBj3//85z+uTp06689bt25dd9pppwX1Y9SoUW7t2rXuggsuCGrHVz7iiCPc1KlT3RZbbLFReyNGjLDVi/3790/pXALUlGQSoIbJlLf6CVCzZPgsNStAzZKwSZrN6Avm7YGAeo0ANbfW19lyqgCASno0n4HCnxzAZG+1sWPH2pYV7LE2fvx49/DDD7u77rrLbbnllq5bt27u/PPPd4sXLzYQnT17tjvppJNcjx49XNOmTW27i6efftpSsFG+/vprd+mll7oKFSq4GTNmuCuuuMLanjlzpuUQJl3bW2+95Xr27OkWLFjg2rVrZ/u5de/e3d15552uVatWtsfbVVdd5R555BE7x6BBgwxcH330Uffbb785vKl33HGHO+OMM5LqKEANG2IZ/YIO64o8qFnQT4CaBVGz2KQANYviZtuD2jcQUHsKUHNrfZ0tpwoApqtWrXK1atVaf95LLrnE7bvvvu7AAw90vXr1cv/4xz/cwIED3WGHHeb23HNPA0py/davX98tW7bM9evXz+AQcOzYsaPr1KmTmzNnjluzZo3r06fP+i0vvvzyS1ejRg2D3LJly7r27du7oUOHuj322MPqsEVGo0aNDHCB2mbNmrkHH3zQ+lG+fHn3448/Wru0OW7cODdkyBC3evVqC0cAkEnt9tlnn1l/5s2bJ0DN0kgSoIYJG3X9BKhh9s11bQFqbhXP6P17ayCg3iBAza31dbacKgCg7rTTTq5hw4brz3vssce6atWquVdffdUdffTRluMXT+qff/7pgEy8nB9++KF5N3/66Sc75vHHHzfIXbp0qU3tjxkzxv3+++/u+uuvX98udevVq+dWrFhhsEmsK8fyU65cOWufeNjXXnvNQJMp+9GjR7sTTzzRlSpVyo674YYbXM2aNd2FF15oULxo0SID02eeecZ+Vq5c6XbYYQdrizJt2jTrb3zRKv70h1lGv6DT70bSmupfmKgC1DD9cl1bgJpbxTP6/dInEFD7CFBza32dLacKJJvipxNvv/22O/zww20aH68n8LjLLrvYFk0NGjSwKXhgE6/qp59+6qpUqWLQ+O9//9s8n4kAtU2bNu6DDz4wz2vt2rXdDz/8YKEAACqxsLSL9/b44493Dz30kJ0rFlA7dOhg5+WH85BvmH1NfQwqHlW8rR5QE4mpKf6wIZbRL+iwriSsrf6FiSpADdMv17UFqLlVPKPfL70DAfVmAWpura+z5VQBABXYAxx9wVu5bt06d8ghh7guXboYJD777LO2Up74UzycxKcSEwqgEkuKFxWQPeuss1zr1q3dwoUL3a+//upuvPHGDTyoRQHqkiVLzKsK5NIuU//ApwdUQPaxxx4zcCY0AIDFk4tXVoCau2GT0S/oLHRb/QsTVYAapl+uawtQc6t4Rr9fegYCal8Bam6tr7PlVAGm71l4FFtYwU8sKAuYXn75ZZu+7927t5s+fbo79NBDLSRgq622Ms8nkMrCKBY/sViKrZ4mTZrkXn/9dQNVFlYBrxSm+IsCVLyeAPMnn3xixxOvCqi+8cYbjlX8eEZZvNW5c2frC17X559/3qbxBai5GzYZ/YLOQrfVvzBRBahh+uW6tgA1t4pn9PulRyCg9heg5tb6OlukFWAaHk8n0/lMsTONDzhSmG4n/tMXABaQTbQ1VFEXCejuuuuuVo8Y14oVK9q5/vjjD4PSROcqjmia4i+OWhsfm9Ev6LCuJKyt/oWJKkAN0y/XtQWouVU8o98v3QIB9S4Bam6tr7NJgSwrIEANEzijX9BhXRGgZkE/AWoWRM1ikwLULIqboOmMfv9dEQioAwWoubW+ziYFsqyAADVM4Ix+QYd1RYCaBf0EqFkQNYtNClCzKG62AfWyQEAdIkDNrfV1NimQZQUEqGECC1ALW7933h/tquy2W9hFZrH294sXq38x+pYqVdpV3a1pyorr/k1Zquy/AHcKBNShAtQwa6q2FIiYAgLUMIPoAVfY+s2eNdpVqRJhQP1+cd70r1LlI8MGSxZq6/4NEzWj+l0QCKgPC1DDrKnaUiBiCghQwwyS0S/osK5k38OxGfbv0YcCH5pZ0Cwfm9xxp3ruxFbvR67run/DTJJR/c4NvNdGClDDrKnaUiBiCghQwwyS0S/osK4IULOgnwA1M6IKUNPTcbP6fjkzEFDHCFDTG2WqVeIKsOE9G9k3btw4p31hayi+ZOrXr5+x87K9FdtWbbvttknbJJEAW16RaKCoIkANM8tm9QAJkyovAVqAmhmjC1DT03Gz+n5pFwioT6YGqGwHybaNZcqUWW+UVJ6p6VkwO7VK/VlUfsjsnFOtZlEB0oiScpT89bks77zzjuvXr5975plnMnJa0qzefffdbvfdd7f9UMkmRcpVXz777DN38803u9KlS7tFixa5Tp06ubPPPjvpuQWoYWbZrB4gYVIJULOgX740KUBNz1Kb1fdLq0BAfbZoQCX1N06qHj16uG7dujmS8FA29UxNz3LZrSVAza6+OW/9m2++cb169XKPPvqoGzZsmKUp3XLLLW2gkpZ07dq17qqrrnKPPPKIa9q0qRs0aJClF43/rFq1avbZ008/7fbee293++23m1f2hBNOcE2aNHFz5851gwcPdhdeeKEDFvmMGyMWUGm7atWqlnWKPqRaAFLe+kgCUKFCBUuruttuu7mePXuubwIgrVmzpuvevbv74osv7Pc1a9ZYkoBERYCaqvqJj9usHiBhUglQs6BfvjQpQE3PUpvV98uJgYD6fNGA+sEHH9jznVTld9xxhwFqKs/U9CyX3VoC1Ozqm/PWfWpR0o7uueeebubMmeZlZOp92bJl7q233nJ9+vRx48aNc0OGDDGobNmy5UafNW/e3PXv39898cQT7sUXX3RjxoxxEydONOA8+OCDXd++fe2NjKl9QLZr165uu+222wBQX3nlFXfTTTe5BQsWrAfkHXfc0U2ZMsXNnj17A23oX4sWLewzjuf3+fPn2/8B3Tlz5rjhw4evr4P3FIgFWvHeko71u+++s2xXAtTMD7vN6gGSefks/KV69epZaDkzTWqKPzM6ClDT0zHq90dG+3dMIKC+lNoUPynEzzrrLAPUVJ6p6Vkuu7UEqNnVN+ete0AF6PgdIMXdP2DAAINJpuHxNuL5JF6V6XHetuI/q1Gjhnv//ffdrFmz3GuvvWawyP8B1MmTJ7vatWu7WrVquaeeesp+B2D5STTFz/mHDh1q5znnnHPcUUcd5ebNm7eBNg0bNnTHH3+8fYZ3tl27duaZpeANnjp1qnmEffn444/Na9u6dWs3ffp09/nnnzsfjzpt2jS77vhy7bXX5tweqZ4wo1+AqZ60GMepf8UQK8GhUddPgBpmX19bgJqejlG/PzLav2aBgDr1v4DKc3LhwoUbCL7XXnu5gw46yD6LBdRUnqnpWS67tQSo2dU35617QMV7ScwmUNagQQMDPgCVqXF+5we3P9PonTt33ugzvJIdO3Y0z+j222/vHnroofWAild21113dTvssIOBYeXKld3zzz9vsa+JAJUw5zfffNPdeuut5u184403Npryx7PqvZ8sjCpXrpwjoJvFT4QpUAhTiC0rV640r+4+++xj/Se8IVnRFH/YUMzoF3RYVxLWVv/CRBWghuknQA3Tb7O6f5sEAuo7/wVUnsnMbsYWnDyE8sUDaqrP1DArZr62ADXzmpZoix5Qb7nlFgM7vJ8TJkxwrVq1citWrLCY0rfffts8mgxwvJtM2cd/BhjuvPPO7u9//7u7+uqrHSED3oMKoAKleEOPPPJI88Z26dLFpthjARWP5ujRo90999xjK/GvvPJK16ZNG3fnnXe6kSNHbqATN1VsjCl9uu+++1ydOnXccccdZ6ECxx57rHv33XftM8IL+FKjLab+8bDGtxl7AgFq2LDcrB4gYVLlJUALUDNjdHlQ09Nxs/p+qRcIqLOKP8WPVZI9U9OzWG5q5QRQ8XQx7co07vLly90BBxyQm6vbDM/iARUvZb169dxOO+1kC4d4gwJS8Zbyw7Q4Xko8n3hC4z9bunSpxa4Qx8oiKab1iVvFq/ree++5SpUqmTf0vPPOs1ABFlqxkAkA9uWiiy5yeE8vu+wy8+IWpwDVflX+iSee6B5//HHzpuLNBVLpOwu2+P+3335rnyWLP+W8AtTiqL/xsZvVAyRMKgFqFvTLlyYFqOlZarP6fqkRCKhfpA6oPENPPfVUM0qyZ2p6FstNrZwAKlOwl1xyiTvmmGMs9rCo7YD8ZY8aNcpWnF9wwWEdNkIAACAASURBVAW5UaJAzkJsJp5NvJ1MkS9ZssTAjel89kVjz1AKU/uAaWyJ/wzwpA7T7/zOSvz4VfK0C8wmgkP+VpzV+/Em4Jy83AC+yQrT+sCx9kHN7gDerB4gWZAy6vrJg5oZowtQ09Mx6vdHRvu3ayCgLk0NUBNZIpVnanoWzE6trAPqjTfeaPtVsjCGvTlZvMKUMFsH4QVjupap22uuucaxPQJAiuft8MMPN68fHjm/ujtWAqaSibPEm4cHjRXn7du3t/aZKmbVGnGJLA7i2Oeee84W3TDtzLkHDhxo8AUI49llqyLiM/FAsvKNeMkQuMqOuTbdKi8D5557rkN3lf8pIA9q2GjI6Bd0WFcS1lb/wkQVoIbp52sLUNPTcbO6f7cOBNQ16QNqetYpuVpZB1S2McIDBhwyJcyUMnGNrDa77rrrDBaJTWQ6+YgjjrAYRTZlJ3aRfS3ZEik2E4KXasSIEVaXxTdAKiCKd7ZRo0a2QS1Q26xZM1u4g1cR8J0xY4YBKXDK7/Tl+++/t3hMVryxVRH1Lr74YoupBPTyraxatcqmvVU2VECAGjYiNqsHSJhUeQnQAtTMGF2Amp6O+n5JT7dCr5V1QEVAgIkMRy+88MJ6QMVzyqpyUnGxsTywSKziySefbNmDWPyCh/P6669PaAMAlbhD9vLEK8o5iHckxpWFQQAxXlVAl/0/ib/AG8sPi4NY/Y0HlvOwVRLeXNpiCpvPaY//qxSGAgLUMDvqAVLY+s2eNdpVqZI8lCbs6sNrf//94rzpX6XKR4ZfcIZb0P0bJmjU9Qu7uujWLjFAZVrf73OJp5QFOEAkG8MTVzh27NhNAiqeUSCUuApWiRNzyWKcfffd1/bUBETZZglA9ccy3c8PgOsBFS8rcYx4YX3Zb7/91u/LGV3zqWepKiBATVWpxMdF/Qta/Quz76ezRxcZ6x3WenjtxYsXp9W/CjmCRY2/MBtLvzD9CrV2JAD16KOPts3YWWjDVPsZZ5zhvv76a/frr78mjaUEMOMBlQVBbH8EsLKlEtCJp3RTgDpp0iTbgoE269at63r37m19waOrUhgKCFDD7KgHSGHrN21kYFxcmDxZqb3voQ+4yjU6ZaXt+EZ1f4TJLP3C9CvU2pEA1AceeMAWSQGHTOszRc82SGQJGj9+vANg40siQGWKH8D95JNP7HCyIQGqLMpK5kFlr1Aglry1pOyklC1b1jaTZ4smlcJQQIAaZkc9QApbPwFqYdtX929h2zfs6qJbOyeAmsrl//jjj7YoKna7IlbxEwbAyvr4wt6Y22yzTcKmmQ4i0xHxrcS5VqxYMZUu2FZMeG5ZwEX++nwseI/ZfL9x48Y57T468yVYv379jJ2XkA3GAOEboUWAGqagHnCFrZ8AtbDtq/u3sO0bdnXRrR0ZQE0mEbniAc74wib08XtyRlfm3PWMxWjE1LKlVy4LOzT069cvYarTdPpBpqi7777bFszx4sDODqRu9YWXGf7fsmVL+6hWrVqO7FnJigA1HSv8r44ecIWtnwC1sO2r+7ew7Rt2ddGtHXlAja500ewZC8zYFYGY3mHDhlm6U/ZzJY896URJfkAowyOPPOKaNm3qBg0aZLG68Z9Vq1bNPmPXAzJJ3X777eaVJXtTkyZN3Ny5c93gwYMtfAIvN5+xpVhsqlParlq1qjvppJOKtacsQMrWYiQOqFChgm0nxlZlsalQP/30U4sVJsNUKvvVClDDxqsecIWtnwC1sO2r+7ew7Rt2ddGtLUCNrm3S6plPdfr6669bmtKZM2dauAJT7ywWI5EBe8uyawLbaAGVeCHjP2vevLntkMCuCi+++KIbM2aMxeoCnCwo69u3r8PLydQ+INu1a1e33XbbbQCo7JJw0003WdIED8hkpZoyZYqbPXv2BtdH/3xCBo7n9/nz59sxgO6cOXPc8OHD19ehLx06dLAtxki0AIDSZ3lQ0xo2m6ykB9wmJSrygKjrJ0AtbPtGffypf2Hjr1BrC1ALzLIeUAE6fgdIiUllCy1gkmn4mjVrmueTeFVCKPCmxn/GAjPSpc6aNcsWrQGL/B9AnTx5sqtdu7ZNq7OHLL8DsPzEelC9tJyfZAichzSsZBUjqUJsATLZGoyCd5YsYH4bMrzBU6dONY+wL8AvfSOhAluSAcx4VUl5Om3aNLvu+MKWY1Et+oIOs4z0C9NPgBqmn8af9AtTQLUTKSBALbBx4QEVgCNGEyhjb1iAD0AlnSu/88NUOtPonTt33ugzYko7duxonlGSFrCnrAdUvLIsQtthhx3c559/blt7kWiB2NdEgMruCmT8In0s7bJDQvy0PJ5Vv0COhVEkTmCRFMBJmAIFL6wvf/zxhy2C42fdunXWHuENhCskKpriDxvoegAXtn4C1MK2r+7fwrZv2NVFt7YANbq2SatnHlBZMATY4f0kAUKrVq1syy1iSt9++23zaAKdeDeZso//DDDceeedLS3t1Vdf7QgZiAVUoBRv6JFHHmneWDyZ33333QaAyvQ7mbzuueceW4lPSltS2ZK9a+TIkRtcH/GxsTGm9Om+++5zZBw77rjjLFSA5A5kD+MzgJOQBY6h7/TFhwQIUNMaOkVW0gMuTNOo6ydALWz7Rn38qX9h469QawtQC8yyHlDxUrLTAXu5stsBXkkgFW8pP+wzi5cSzyee0PjPli5d6k477TSLY2WRFNP6xK3iVX3vvfdcpUqVzBt63nnnWagAnksWMgHAvlx00UWWfpaEB3hxi1OA6rPPPtuqsKUYi6GAZry5QCp9Jk4Vzyk/ACyLsZIVeVCLo/7Gx+oBUtj6CVAL2766fwvbvmFXF93aAtTo2iatnpGQAG8i3k6myMmuxdQ50/kkQShfvry1y9Q+kBdb4j8DPKnD9Du/A4LxW3vRLjAbu3+tb5O/pbLCPtmFcs6VK1cWmeLQZw/blFgC1E0pVPTf9YArbP0EqIVtX92/hW3fsKuLbm0BanRtk1bP9tlnH3fuuecmTRGbVqMFUEmAGmZEPeAKWz8BamHbV/dvYds37OqiW1uAGl3bpNWzVatW2TS4yoYKCFDDRoQecIWtnwC1sO2r+7ew7Rt2ddGtLUCNrm3UswwqIEANE1MPuMLW79PZo4sMpQm7+vDaZBMkxr24pULlI4tbJa3jdX+kJdv6StIvTL9CrS1ALVTL6ro2UECAGjYg9AAppn7fvr5BhXQBq5hnTfvwr/6o5qpXr552/WxX1PgLU1j6FbZ+YVcX3doC1OjaRj3LoAIC1DAx9YArpn6vXObch/cXs1LJHf7VKfMFqAHy6/4IEM85J/3C9CvU2gLUQrWsrkse1AyOAT1AiimmALWYghV9uMZfmJzSr7D1C7u66NYWoGbBNuz9yQb5bDbPPqQUpvhYwMS+oqQhbdKkSbHOTHtHHHGEK1OmTLHqRe1gto4ilWlxrz/0OuRBDVNQD7hi6idALaZgAtSMChbXmO7fMHWjrl/Y1UW3tgA1C7YhDzwweeONN7o+ffrYGR577DHbYJ6sTGRE8nnmizr9qFGj3Nq1a90FF1xgG+OTa75ixYpZ6HHummTPVFKiXn/99bk7qXOWeYq0r1EtUf8CVP+KOXIEqMUUTICaUcEEqBmVM+rffxm92Ag1JkDNgjEuueQS85QOGTLELVq0yJUuXXqTgPrwww+73r17W2+A2mbNmrnDDz/cMkCRnemss85yrVu3tt8bNmxoKUS32247ywTVo0cP24iftKRkX7r55psNbMm+BAj7zfLx4gK7s2fPtqxLDzzwgPvxxx8tOxSZp4Bq0p+S5/7SSy91FSpUcDNmzHBXXHGFe+mll9zMmTPdbbfd5sgQddVVV7l169a5iRMn2uraRx991DJODRs2zFKscs5u3bo5UpjG9ufll192vXr1sv6PGTPG9e3b1/3666+uX79+rl27dna+iy++2C1YsMCyVNEW53jllVcsg9W3337r+vfv79q3b5+wfjJzClDDBnrUv6Aj1z8BatiAE2BJv4wqENZY5L5fwi4nb2oLUDNsqtWrV9tig4ULFxrwkYeef4vyoAJobLA/duxYR31y25OyFDhbs2aNASupRE899VTXvXt324gfbyCQufvuuxuokfHpjDPOcLNmzTKAA+gAZLy1vpAOlCl24BIoBULJOPXNN98YNOLx3X///d3pp5/uatSo4YDmsmXLGgwOHTrU+tCpUyc73qcVHT58uBs8eLD1s2fPngbmgCxQXr9+fbds2TJ3+eWXr+8P2pBy1adL/fDDD90PP/xg4Dxv3jzTCsg+7LDD7DP0Qwsg+c0337R6AwYMSFpfgJrhAf1Xc1H/go5c/wSoGR2IkbOvAFr2zagCaiyRAgLUDI8LQBQwZAp75MiRlk6U/xcFqHhBgUy8kBQAFG/i559/buBJW0zxT5482dWrV89dd911BouHHHKIHeuny++99153zTXXuFdffdWOA0BjC8CIV7VWrVqWnhQv6ymnnLL+M7ytwOlTTz1l9VesWGEe1sqVK9ux/JQrV84RYwug4kkFmr/77jvXtGlTA0l+CHEAPAHJn376yTy8vj/8HUD96KOPXOPGja1tUrO2bNnS2geQAVYK3lxSt9auXdvCI9Dxl19+sUQEpHGNr49HmTJt2jTrQ3zRFH/6g12AUEztBKjFFKzowzX+wuSUfoWtX9jVRbe2ADXDtjn66KPdNttsY9PjQBTwCXAxNZ8sBvX+++83GAXoKEznt23b1rbeiAVUH4PKVP6uu+5qP4QFxIIo58ejedxxx7k2bdqsvzqgkj7RRpUqVcyTChBWrVrVzZ8/3+2yyy72N+o8++yz9u8HH3xgHlAAkWsg3CAWUImnbd68uU3HsyCM/tIOINigQQODbA+ovj8eUPGWAsl4jZ988klrn3/x1vI3CuEC/E7bgCqeYfq97bbbGiTH1//kk0/Mc5uoaIo/bKDrAVdM/QSoxRRMgJpRweIa0/0bpm7U9Qu7uujWFqBm0DZM6x900EHmefRxn0xZM6UO3CUD1C+++MI8kfwdD2GjRo1spf+IESMsPpOp99hFUh5QO3To4OrWresAsy222MKg8pZbbrHY0nhA5TLxVAKwxIX6mFZgtEWLFuaJJR4UoGQaPxVAJRaWvuG5BW6POeYYC0tgx4EJEyaYpxQtANZ4QMXLS38mTZrkSpUqZYu/uA48rXg/8RDjzaUvy5cv3whQlyxZslF9D98C1AwO6r+aivoXdOT6J0DN6CCMnH0FgLJvRhVQY4kUEKBmcFwAeCziAdh8wSvKlDPAxwIgYDXRKn4WFDH9DsgCkAMHDnQsKGJh1Pjx4x0wGutBBVi7dOliC5BYFb9y5Up38sknO1b+s0iLKXPqxhbiU/G2sliKqXTgkM9YlARIsjgKcNx6661TAlSAEhD/+eef3dSpU22xFIDJ1lpbbbWVXQuQiia+P7EeVAB1ypQpFrdKuAJgTVgEIQv0hXhVFmeNGzcuoQc1Uf1k5pQHNWygCxCKqZ8AtZiCyYOaUcEE0BmVM+rffxm92Ag1JkCNkDHwXhIW4PdOpWtAHrCHhzRZYX9VPK1M3adSAEpiY2MLuw2w4Mp7fjfVDjGoxMKyuItQA98/+o93k76wswAhCuXLl0/aHOcl9CC2P8ApU/54UfGuFlUS1U90vAB1UxYVIIQpFFdbgJpROaMOCOpfmLmlX5h+hVpbgFqols3ydXlAJYQhH4oANcxKeoAUUz8BajEF0wtSRgWLa0z3b5i6Udcv7OqiW1uAGl3bRLpnxMqyGCxVj2tJX4wANcwCUf+Cjlz/BKhhA06AJf0yqkBYY5H7fgm7nLypLUDNG1OpoyEKCFBD1HO2QwMxwVEtkevft69vIBVx38RoR7V89Uc12TfAOJEbfwL8AGtuXDXq9s3oxUaoMQFqhIyhrmRPAbJRHXDAAdk7gVqWAlJACkiBglUgyi/ohSq6ALVQLavrkgJSQApIASkgBaRAniogQM1Tw6nbUkAKSAEpIAWkgBQoVAUEqIVqWV2XFJACUkAKSAEpIAXyVAEBap4aTt2WAlJACkgBKSAFpEChKiBALVTL6rqkgBSQAlJACkgBKZCnCghQ89Rw6rYUkAJSQApIASkgBQpVAQFqoVpW1yUFpIAUkAJSQApIgTxVQICap4ZTt6WAFJACUkAKSAEpUKgKCFAL1bK6LikgBaSAFJACUkAK5KkCAtQ8NZy6LQWkgBSQAlJACkiBQlVAgFqoltV1SQEpIAWkgBSQAlIgTxUQoOap4dRtKSAFpIAUkAJSQAoUqgIC1EK1rK5LCkgBKSAFpIAUkAJ5qoAANU8Np25LASkgBaSAFJACUqBQFRCgFqpldV1SQApIASkgBaSAFMhTBQSoeWo4dVsKSAEpIAWkgBSQAoWqgAC1UC2r65ICUkAKSAEpIAWkQJ4qIEDNU8Op21JACkgBKSAFpIAUKFQFBKiFalldlxSQAlJACkgBKSAF8lQBAWqeGk7dlgJSQApIASkgBaRAoSogQC1Uy+q6pIAUkAJSQApIASmQpwoIUPPUcOp28RSYOHGiO+CAA4pXSUdLgc1EgRk/vLuZXKkuUwoUT4Eypcu4Q3ap56pXr168ijo6WAEBarCEaiAfFPjnP//prr322sh29auvvor0F6D6FzZ0oq7fljeVCrtA1ZYCBarAmxe+5SqtrRzp7+cCld4JUAvVsrquDRQQoIYNiKgDlvoXZl8Baph+ql24CghQS862AtSS077Ezjx37ly3fPnyDc5fuXLlvJoC//bbb90vv/zi9t9//5R0FKCmJFPSgwSAha2fADXMvqpduAoIUEvOtgLUktO+xM7ctm1b95///MfVqVNnfR/q1q3rTjvttKA+jRo1yq1du9ZdcMEFQe34ykcccYSbOnWq22KLLTZqb8SIEe7jjz92/fv3T+lcAtSUZBKghsmUt/oJULNkeDWb9woIUEvOhALUktO+xM4MoJ555pmOf2MLgDlhwgQ3duxY99JLL7nBgwe78ePHu4cfftjdddddbsstt3TdunVz559/vlu8eLGB6OzZs91JJ53kevTo4Zo2ber+9a9/uaefftq1aNHCmv7666/dpZde6ipUqOBmzJjhrrjiCmt75syZ7rbbbnMXXXSRe+utt1zPnj3dggULXLt27dyAAQNc9+7d3Z133ulatWrlnnzySXfVVVe5Rx55xM4xaNAgA9dHH33U/fbbbw5v6h133OHOOOOMpJoKUMOGmzyoha2fADXMvqpduAoIUEvOtgLUktO+xM4MmK5atcrVqlVrfR8uueQSt++++7oDDzzQ9erVy/3jH/9wAwcOdIcddpjbc889DShLly7t6tev75YtW+b69etncAg4duzY0XXq1MnNmTPHrVmzxvXp08eVKVPG2v7yyy9djRo1DHLLli3r2rdv74YOHer22GMPq/PNN9+4Ro0aGeACtc2aNXMPPvig9aN8+fLuxx9/tHZpc9y4cW7IkCFu9erVFo4AIL/77rvus88+s/7MmzdPgJqlUSVADRM26voJUMPsq9qFq4AAteRsK0AtOe1L7MwA6k477eQaNmy4vg/HHnusq1atmnv11Vfd0Ucf7U4//XTzpP75558GmXg5P/zwQ/Nu/vTTT3bM448/bpC7dOlSm9ofM2aM+/33393111+/vl3q1qtXz61YscJgk1hXjuWnXLly1j7xsK+99pqBJlP2o0ePdieeeKIrVaqUHXfDDTe4mjVrugsvvNCgeNGiRQamzzzzjP2sXLnS7bDDDtYWZdq0adbf+KJV/OkPuagDlvqXvm2pKUAN00+1C1cBAWrJ2VaAWnLal9iZk03x06G3337bHX744TaNj9cTeNxll11si6YGDRrYFDywiVf1008/dVWqVDFo/Pe//22ez0SA2qZNG/fBBx+Y57V27druhx9+sFAAAJVYWNrFe3v88ce7hx56yM4VC6gdOnSw8/LDeX7++WfHvqY+BhWPKt5WD6iJhNUUf9hwEwAWtn4C1DD7qnbhKiBALTnbClBLTvsSOzOACuwBjr7grVy3bp075JBDXJcuXQwSn332WVspT/wpHk7iU4kJBVCJJcWLCsieddZZrnXr1m7hwoXu119/dTfeeOMGHtSiAHXJkiXmVQVyaZepf+DTAyog+9hjjxk4ExoAwOLJxSsrQM3dEBKghmkddf0EqGH2Ve3CVUCAWnK2FaCWnPYldmam71l4FFtYwU8sKAuYXn75ZZu+7927t5s+fbo79NBDLSRgq622Ms8nkMrCKBY/sViKrZ4mTZrkXn/9dQNVFlYBrxSm+IsCVLyeAPMnn3xixxOvCqi+8cYbjlX8eEZZvNW5c2frC17X559/3qbxBai5G0JRByz1L2wsCFDD9FPtwlVAgFpythWglpz2eXNmpuHxdDKdzxQ70/iAI4XpduI/fQFgAdlEW0MVdcGA7q677mr1iHGtWLGineuPP/4wKE10ruIIqCn+4qi18bECwMLWT4AaZl/VLlwFBKglZ1sBaslprzPnUAEBapjYAtTC1k+AGmZf1S5cBQSoJWdbAWrJaa8z51ABAWqY2ALUwtZPgBpmX9UuXAUEqCVnWwFqyWmvM+dQAQFqmNgC1MLWb8z00W633XYLu8gs1iYESP1LX2Dpl752W22xlau0trKrXr16+o2oZloKCFDTkk2V8k0BAWqYxQSoha3fpHcFqCEWFgCGqOdssW1xX0AOqXZk2EmLUTvq33/FuJS8OlSAmlfmUmfTVUCAmq5y/60X9S9o9S/Mvkf8s1RYA6otBXKowMD2r7p61Zrn7IxR/37JmRA5PpEANceC63Qlo4AANUz3qH9Bq39h9hWghumn2rlVQICaW71L6mwC1JJSPkvnZcN7NrJv3Lhxls6QuFm2hgIS6tevn7Hzsr0V21Ztu+22SdskkQBbXpFooKgiQA0ziwCwsPUToIbZV7Vzq4AANbd6l9TZBKglpXyWzksaUVKOkr8+l+Wdd95x/fr1c88880xGTkua1bvvvtvtvvvuth8q2aRIuerLZ5995m6++WZXunRpt2jRItepUyd39tlnJz23ADXMLALUwtZPgBpmX9XOrQIC1NzqXVJnE6CWlPJZOu8333zjevXq5R599FE3bNgwS1O65ZZbum7dulla0rVr17qrrrrKPfLII65p06Zu0KBBll40/rNq1arZZ08//bTbe++93e23325e2RNOOME1adLEzZ071w0ePNhdeOGFDljks9WrV28AqLRdtWpVyzpFH1ItAGmZMmUsCUCFChUsrSoB9D179lzfBEBas2ZN1717d/fFF1/Y72vWrLEkAYmKADVV9RMfJ0AtbP0EqGH2Ve3cKiBAza3eJXU2AWpJKZ+l8/rUoqQd3XPPPd3MmTPNy8jU+7Jly9xbb73l+vTp48aNG+eGDBliUNmyZcuNPmvevLnr37+/e+KJJ9yLL77oxowZ4yZOnGjAefDBB7u+ffs6vJxM7QOyXbt2ddttt90GgPrKK6+4m266yS1YsGA9IO+4445uypQpbvbs2RsoQP9atGhhn3E8v8+fP9/+D+jOmTPHDR8+fH0dvKdALNCK95Z0rN99951luxKgZn5wCVDDNI26fgLUMPuqdm4VEKDmVu+SOpsAtaSUz9J5PaACdPwOkBKTOmDAAINJpuHxNuL5JF6V6XG8qfGf1ahRw73//vtu1qxZ7rXXXjNY5P8A6uTJk13t2rVdrVq13FNPPWW/A7D8JJri5/xDhw6185xzzjnuqKOOcvPmzdtAgYYNG7rjjz/ePsM7265dO/PMUvAGT5061TzCvnz88cfmtW3durWbPn26+/zzz52PR502bZpdd3y59tprs6R6eLNRBxj1L8zGUddPgBpmX9XOrQIC1NzqXVJnE6CWlPJZOq8HVLyXxGwCZQ0aNDDgA1CZGud3fphKZxq9c+fOG32GV7Jjx47mGd1+++3dQw89tB5Q8cruuuuubocddjAwrFy5snv++ect9jURoP7555/uzTffdLfeeqt5O994442NpvzxrHrvJwujypUr51gkxeInwhQohCnElpUrV5pXd5999rH+E96QrGiKP2zARR2w1L8w+wpQw/RT7dwqIEDNrd4ldTYBakkpn6XzekC95ZZbDOzwfk6YMMG1atXKrVixwmJK3377bfNoAp14N5myj/8MMNx5553d3//+d3f11Vc7Qga8BxVABUrxhh555JHmje3SpYtNsccCKh7N0aNHu3vuucdW4l955ZWuTZs27s4773QjR47cQAHiY2NjTOnTfffd5+rUqeOOO+44CxU49thj3bvvvmufEV4AlNAWU/94WOPbjD2BADVswAkAC1s/AWqYfVU7twoIUHOrd0mdTYBaUspn6bweUPFS1qtXz+200062cAivJJCKt5QfpsXxUuL5xBMa/9nSpUvdaaedZnGsLJJiWp+4Vbyq7733nqtUqZJ5Q8877zwLFWChFQuZAGBfLrroIof39LLLLjMvbnEKUO1X5Z944onu8ccfN28q3lwglb6zYIv/f/vtt/ZZsvhTzitALY76Gx8rQC1s/QSoYfZV7dwqIEDNrd4ldTYBakkpn6XzEpuJZxNvJ1PkS5YsMXBjOv/333+3PUMpTO0DprEl/jPAkzpMv/M7K/HjV8nTLjCbCA75W3FW78dLwjmZxi8qBR7T+sCx9kHN0oD6q1kBapi+UddPgBpmX9XOrQIC1NzqXVJnK3FAJS6SqV9WirMSe8SIEe7ll1+2xTtMLTMt3aFDB/fCCy/Yvpdsk4Q3jRXmTDvjzcODxv/bt29vi2OYKmYlOHGJLA5i2vm5556zRTdMO7Nt0cCBAw2+Ro0a5Vigw1ZFxGfigTzrrLMsXjIErkrKoMRjnnvuue7GG28sqS5E8rzyoIaZJeqApf6F2VeAGqafaudWAQFqbvUuqbOVOKCy1RFxkLfddpst6GnWrJl56wBG4G7GVgAAIABJREFUoJON55kexlMGQLL4hzrff/+9gSaLb4BUQJSV4Y0aNXI9evSwbYpoi4U7eBUvuOACN2PGDANS4JTfmd6mHeIx27Zta4BMvYsvvthiKgG9fCurVq2yaW+VDRUQoIaNCAFgYesnQA2zr2rnVgEBam71LqmzlTig4hk9/fTTDSpPOeUUd9BBBxlUJgNU4iovvfRS87QSdwis4hUFyoh3XL58uS0MwlsK4LJIh/0/iWkkPpIfFgex+hvYZZENWyUR00hbTGHzOe3xf5XCUECAGmZHAWph6zfp3dFFhtKEXX147cWLF6t/ATIWon6HVDsyQJHiVY3691/xriZ/ji5xQEUqgJBpePbJBB6Z9veACmji1fQeVFZ0sxIcQMUzCoTicWWVODGXeFv33Xdf21OTtvDKAqj+WM7DD/U9oALExDHihfVlv/32W78vZ/6YUz1NpoAANWxsRP0LWv0Ls+/8OdEB1HKVNgYP2TfMvtKvsPULu7ro1i5xQCVWkixCeE/vvfdeA0lScJJ7ffz48eZNZfo/FUBlQRDbHwGsxK4CnXhKNwWokyZNsq2WgNa6deu63r1726IfVp+rFIYCAtQwO+oBV9j6fTaqVNgFZqh2mfJ7u+qnfrVRaxp/YQJLv8LWL+zqolu7xAGVWFA8pEzt4zklaxBwyBZF7LcJOH7yySfrAZW0nGQPSuRBZYqfWFKOp5ANCVBln85kHlT2CgVin332WUvZSSlbtqxtJs8WTSqFoYAANcyOesAVtn4C1MK2r+7fwrZv2NVFt3aJAyrS4PFksRIr0GO3C2KLoQoVKhRbPeJtyHS0xRZbGPRWrFgxpTbYFunrr792e+21l+Wvz8eCliw6a9y4cU67j858CeINz1QhZIP9WwnfCC0C1DAF9YArbP0EqIVtX92/hW3fsKuLbu1IAGp05cm/nrHrAWERN9xwQ047z6b9/fr1S5jqNJ2OkCnq7rvvdrvvvrvt4UrIB6lbfVmzZo39H486pVatWo7sWcmKADUdK/yvjh5wha2fALWw7av7t7DtG3Z10a0tQI2ubdLqGbG6vXr1slCJYcOGWbpT9nMljz3pRNlHllAGFqQR6zto0CCL1Y3/rFq1avYZux6QSer22283ryzZm5o0aeLmzp3rBg8ebOETLGTjs9WrV28AqLRdtWpVd9JJJxVrT1mAtEyZMpZMAA8624mxWX9sKtRPP/3UYoXZEzeV/WoFqGkNp/WV9IArbP0EqIVtX92/hW3fsKuLbm0BanRtk1bPfKpTkhiQppQ4XsIVmHpnsRiJDPr06WNpS9lGC6jECxn/WfPmzW2HhCeeeMKSKIwZM8ZidQFO4oL79u3r8HIytQ/Idu3a1W233XYbACq7JNx0002WNMEDMlmppkyZ4mbPnr3B9dE/YpEpHM/v8+fPt/8DunPmzHHDhw9fX4e+kMCBLcZItACA0md5UNMaNpuspAfcJiUq8oCo6ydALWz7Rn38qX9h469QawtQC8yyHlABOn4HSIlJZQstYJJp+Jo1a5rnk3hVMnbhTY3/jAVmpEudNWuW7SsLLPJ/AHXy5Mmudu3aNq3OHrL8DsDywxZe8YXzkwyB85CG9aijjrKkCrEFyGRrMAreWbKA4Zml4A2eOnWqeYR9AX7pGwkVxo4da8CMV5UY5mnTptl1xxe2HItq0Rd0mGWkX5h+AtQw/TT+pF+YAqqdSAEBaoGNCw+oABwxmkAZe8MCfAAq2bj4nR+m0plG79y580afEVPasWNH84yStIA9ZT2g4pVlEdoOO+zgPv/8c9vai6xcxL4mAlR2VyDjF+ljaZcdEuKn5fGssnsDhYVRJE5gkRTASZgCBS+sL3/88YctguNn3bp11h7hDYQrJCqa4g8b6HoAF7Z+AtTCtq/u38K2b9jVRbe2ADW6tkmrZx5QWTAE2OH9JItWq1atbMstYkrffvtt82gCnXg3mbKP/www3Hnnnd3f//53d/XVVztCBmIBFSjFG3rkkUeaNxZP5nfffbcBoDL9Tiave+65x1biX3nllZZkgexdI0eO3OD6iI+NjTGlT/fdd5+rU6eOIzkDoQLHHnusZQ/jM4CTkAWOoe/0xYcECFDTGjpFVtIDLkzTqOsnQC1s+0Z9/Kl/YeOvUGsLUAvMsh5Q8VKSFpa9XEnfilcSSMVbys/06dPNS4nnE09o/GdLly51p512msWxskiKaX3iVvGqvvfee65SpUrmDT3vvPMsVADPJQuZAGBf2MsW7ykJD/DiFqcA1WeffbZVOfHEE20xFNCMNxdIpc/EqeI55QeAZTFWsiIPanHU3/hYPUAKWz8BamHbV/dvYds37OqiW1uAGl3bpNUzEhLgTcTbyRQ52bWYOmc6//fff3fly5e3dpnaB/JiS/xngCd1mH7nd0AQ2I0ttAvM+un5+L+lssI+2YVyTvbCBXyTFZ89bFNiCVA3pVDRf9cDrrD1E6AWtn11/xa2fcOuLrq1BajRtU1aPSPZwbnnnutIIavyPwUEqGGjQQ+4wtZPgFrY9tX9W9j2Dbu66NYWoEbXNmn1bNWqVTYNrrKhAgLUsBGhB1xh6ydALWz76v4tbPuGXV10awtQo2sb9SyDCghQw8TUA66w9Zs/Z3SRoTRhV1+82uUqHblRBY2/4mkYf7T0K2z9wq4uurUFqNG1jXqWQQUEqGFiRvYBt+B1u7DF3y92u1VJHqscdvXhtaPev6/+rOaqV68efqFZaiGy4++v61X/wgwv/cL0K9TaAtRCtayuawMFBKhhAyKyD5DxFzv33v8SOIRd5eZb+6uO8wWoAeaP7P0hgA6w6v+qRt2+GbnICDYiQI2gUdSlzCsgQA3TNLJf0ALUMMN6gBGgBukY2ftDgBpkV1856vbNyEVGsBEBahaMwt6fbJDPZvPsQ0pZvHixYwET+4qShrRJkybFOjPtHXHEEa5MmTLFqhe1g9k6ilSmxb3+0OsQoIYpGNkvaAFqmGEFqJnR76uv5IEOUDKy3y95AvgB0ke6qgA1C+YhDzwwyVZPffr0sTM89thjtsE8WZnIiOTzzBd1+lGjRrm1a9e6Cy64wDbGJ9d8xYoVs9Dj3DXJnqmkRL3++utzd1LnLPMUaV+jWvQFnaZlBKhpCrdhNU3xh8mo+1f6hSmg2okUEKBmYVxccskl5ikdMmSIW7RokStduvQmAfXhhx92vXv3tt4Atc2aNXOHH364ZYAiO9NZZ53lWrdubb83bNjQUohut912lgmqR48ethE/aUnJvnTzzTcb2JJ9CRD2m+XjxQV2Z8+ebVmXHnjgAffjjz9adigyTwHVpD8lz/2ll17qKlSo4GbMmOGuuOIK99JLL7mZM2e62267zZEh6qqrrnLr1q1zEydOtNW/jz76qGWcGjZsmKVY5ZzdunVzpDCN7c/LL7/sevXqZf0fM2aM69u3r/v1119dv379XLt27ex8F198sVuwYIFlqaItzvHKK69YBqtvv/3W9e/f37Vv3z5h/WTmFKCGDfTIPoAFqGGGlQc1M/rJgxqkY2S/X+RBDbJraGUBaqiCcfVXr15tUz0LFy404CMPPf8W5UEF0Nhgf+zYsY765LYnZSlwtmbNGgNWUomeeuqprnv37rYRP95AIHP33Xc3UCPj0xlnnOFmzZplAAfQAch4a30hHShT7MAlUAqEknHqm2++MWjE47v//vu7008/3dWoUcMBzWXLljUYHDp0qPWhU6dOdrxPKzp8+HA3ePBg62fPnj0NzAFZoLx+/fpu2bJl7vLLL1/fH7Qh5apPl/rhhx+6H374wcB53rx5phWQfdhhh9ln6IcWQPKbb75p9QYMGJC0vgA1wwM66l/QAtSMGFwe1DAZBVjSL0wB1ZYHNQdjABAFDJnCHjlypKUT5f9FASpeUCATLyQFAMWb+Pnnnxt40hZT/JMnT3b16tVz1113ncHiIYccYsf66fJ7773XXXPNNe7VV1+14wDQ2AIw4lWtVauWpSfFy3rKKaes/wxvK3D61FNPWf0VK1aYh7Vy5cp2LD/lypVzxNgCqHhSgebvvvvONW3a1ECSH0IcAE9A8qeffjIPr+8PfwdQP/roI9e4cWNrm9SsLVu2tPYBZICVgjeX1K21a9e28Ah0/OWXXywRAWlc4+vjUaZMmzbN+hBfNMWf/g0Q2QewADV9o8bUFKCGyRjZ+yPqL5jqX9jAK/Da8qBm2MBHH32022abbWx6HIgCPgEupuaTxaDef//9BqMAHYXp/LZt2zq+9GIB1cegMpW/66672g9hAbEgyvnxaB533HGuTZs2668OqKRPtFGlShXzpAKEVatWdfPnz3e77LKL/Y06zz77rP37wQcfmAcUQOQaCDeIBVTiaZs3b27T8SwIo7+0Awg2aNDAINsDqu+PB1S8pUAyXuMnn3zS2udfvLX8jUK4AL/TNqCKZ5h+b7vttgbJ8fU/+eQT89wmKpriDxvokX0AC1DDDOsBQav4g3SM7P0hAAyyq68cdftm5CIj2IgANYNGYVr/oIMOMs+jj/tkypopdeAuGaB+8cUX5onk73gIGzVqZCv9R4wYYfGZTL3HLpLygNqhQwdXt25dB5htscUWBpW33HKLxZbGAyqXiacSgCUu1Me0AqMtWrQwTyzxoAAl0/ipACqxsPQNzy1we8wxx1hYAjsOTJgwwTylaAGwxgMqXl76M2nSJFeqVClb/MV14GnF+4mHGG8ufVm+fPlGgLpkyZKN6nv4FqBmcFBH/QEnQM2IseVBDZMx6gCj/hW2fcOuLrq1BagZtA2AxyIegM0XvKJMOQN8LAACVhOt4mdBEdPvgCwAOXDgQMeCIhZGjR8/3gGjsR5UgLVLly62AIlV8StXrnQnn3yyY+U/i7SYMqdubCE+FW8ri6WYSgcO+YxFSYAki6MAx6233jolQAUoAfGff/7ZTZ061RZLAZhsrbXVVlvZtQCpaOL7E+tBBVCnTJlicauEKwDWhEUQskBfiFdlcda4ceMSelAT1U9mTnlQwwZ6ZB9wAtQww8qDmhn9tEgqSMfIfr9E/QU9SPXoVxagRshGeC8JC/B7p9I1IA/Yw0OarLC/Kp5Wpu5TKQAlsbGxhd0GWHDlPb+baocYVGJhWdxFqIHvH/3Hu0lf2FmAEIXy5csnbY7zEnoQ2x/glCl/vKh4V4sqieonOl6AuimLFv33yD5ABKhhhhWgZkY/AWqQjpH9fhGgBtk1tLIANVTBzbS+B1RCGPKhCFDDrBTZB4gANcywAtTM6CdADdIxst8vAtQgu4ZWFqCGKriZ1idWlsVgqXpcS1omAWqYBSL7ABGghhlWgJoZ/QSoQTpG9vtFgBpk19DKAtRQBVU/LxQQoIaZKbIPkAWv24Ut/n6x263KbmEXmcXaUe/fV39WU6rOAPtH9v7IE8CSfgGDr4CrClAL2Li6tP8pQDaqAw44QJJIASkgBaSAFCi2AizaVcmtAgLU3Oqts0kBKSAFpIAUkAJSQApsQgEBqoaIFJACUkAKSAEpIAWkQKQUEKBGyhzqjBSQAlJACkgBKSAFpIAAVWNACkgBKSAFpIAUkAJSIFIKCFAjZQ51RgpIASkgBaSAFJACUkCAqjEgBaSAFJACUkAKSAEpECkFBKiRMoc6IwWkgBSQAlJACkgBKSBA1RiQAlJACkgBKSAFpIAUiJQCAtRImUOdkQJSQApIASkgBaSAFBCgagxIASkgBaSAFJACUkAKREoBAWqkzKHOSAEpIAWkgBSQAlJACghQNQakgBSQAlJACkgBKSAFIqWAADVS5lBnpIAUkAJSQApIASkgBQSoGgNSQApIgf9v7z3ApCqy9/9DDiJhkSiKgoAgKlkEfrAsSFgRkBxEyaACK0gQUIKKIiKsgEtOkiUJO2AgSVIBkSRJclIyQ5b8f97z3zvfZugZurt6umtuv/U88wz03FP31HtO9/30qbq3qAAVoAJUgApYpQAB1apw0BkqQAWoABWgAlSAClABAipzgApQASpABagAFaACVMAqBQioVoWDzlABKkAFqAAVoAJUgAoQUJkDVIAKUAEqQAWoABWgAlYpQEC1Khx0hgpQASpABagAFaACVICAyhygAlSAClABKkAFqAAVsEoBAqpV4aAzVIAKUAEqQAWoABWgAgRU5kBEKBAVFSWFChWKiLFykFSAClABKhBcBfLkyRPcDtnbfRUgoN5XIh7gBgU++eQT6dGjh7VD2b9/v9j8AUj/zFLHdv2SvJ3bbIC0pgIuVmDfmyus/nx2q/QEVLdGluO6SwECqllC2A5Y9M8svgRUM/1o7W4FCKjhiS8BNTy686whVoCAaiY4AdDd+hFQzeJLa3crQEANT3wJqOHRnWcNsQIEVDPBCaju1o+AahZfWrtbAQJqeOJLQA2P7mE969atW+X06dN3+ZA9e/ZEdRPR0aNH5eLFi1KwYEGftCSg+iRTnAcRUN2tHwHVLL60drcCBNTwxJeAGh7dw3rWevXqye3bt+Xpp5+O8aNIkSLy8ssvG/k1depUuXHjhrRo0cKoH8e4XLlysnLlSkmWLNk9/U2aNEl27NghgwYN8ulcBFSfZCKgmsmUaPUjoCZQ4NmtKxQgoIYnjATU8Oge1rMCUBs1aiT47dkAmAsXLpRZs2bJkiVLZMSIETJ//nyZOHGiDB06VJInTy6dO3eW5s2by59//qkgumnTJqlRo4Z0795dypcvL1evXpV58+ZJ5cqVtevDhw9L+/btJUOGDLJ+/Xrp1KmT9r1hwwYZMGCAtG7dWtauXSs9e/aUAwcOSP369WXIkCHStWtX+eyzz6RWrVoye/Zs6dKli0yePFnPMXz4cAXXKVOmyJUrVwTV1MGDB0vDhg3j1JWAapZyrKC6Wz8Cqll8ae1uBQio4YkvATU8uof1rADTCxcuSIECBWL8aNeunTzxxBPy1FNPSa9eveSjjz6SYcOGSZkyZSR37twKlEmTJpXixYvLqVOnZODAgQqHAMdWrVpJ27ZtZfPmzXLt2jXp16+fpEiRQvveu3ev5MuXTyE3derU0rhxYxkzZozkypVLbY4cOSKlSpVSwAXUVqhQQcaOHat+pEuXTs6cOaP9os85c+bIyJEj5fLly7ocAYC8bt062b17t/qzfft2AmoCZRYB1UxY2/UjoJrFl9buVoCAGp74ElDDo3tYzwpAzZw5s5QsWTLGjypVqsijjz4qy5cvl0qVKkmDBg20knrnzh2FTFQ5t23bptXNc+fO6THTp09XyD158qRO7c+cOVP++usv6d27d0y/sC1WrJicPXtWYRNrXXEsftKmTav9Yz3sihUrFDQxZT9jxgx58cUXJUmSJHrcu+++K/nz55eWLVsqFB86dEjB9Ouvv9af8+fPS8aMGbWvuBorqGYpZztg0T+z+BJQzfSjtbsVIKCGJ74E1PDoHtazxjXFD6d+/PFHKVu2rE7jo+oJeMySJYs+5L5EiRI6BQ/YRFV1165dkjNnToXGmzdvauXTG6DWrVtXtmzZopXXwoULy4kTJ3QpAAAVa2HRL6q31atXlwkTJui5PAG1SZMmel784DzR0dGCnaGcNaioqKLa6gDqmjVrFKhjNz6oP/C0IwAGrh0sbdePgGoWX1q7WwECanjiS0ANj+5hPSsAFbAHcHQaqpW3bt2SokWLSocOHRQSFyxYoHfKY/0pKpxYn4o1oQBUrCVFFRUg27RpU6lTp44cPHhQLl26JH379r2rghofoB4/flyrqoBc9Iupf8CnA6gA2WnTpik4Y2kAABaVXFRl4wJUb+KygmqWcrYDFv0ziy8B1Uw/WrtbAQJqeOJLQA2P7mE9K6bvceORZ8Md/FgLihuYli5dqtP3ffr0kZ9++kmef/55XRKQMmVKrXwCUnFjFG5+ws1SeNTT4sWL5YcfflBQxY1VgFc0TPHHB6ioegKYd+7cqcdjvSpAddWqVYK7+FEZxc1bb7zxhvqCquuiRYsEVVICaujSiABoprXt+hFQzeJLa3crQEANT3wJqOHRPVGdFdPwqHRiOh9T7JjGBziiYbod6z+dBoAFyHp7NFR8gwboZs2aVe2wxjVTpkx6ruvXryuUejuXPyKyguqPWvceaztg0T+z+BJQzfSjtbsVIKCGJ74E1PDozrOGWAECqpngBEB360dANYsvrd2tAAE1PPEloIZHd541xAoQUM0EJ6C6Wz8Cqll8ae1uBQio4YkvATU8uvOsIVaAgGomOAHV3foRUM3iS2t3K0BADU98Cajh0Z1nDbECBFQzwQmo7taPgGoWX1q7WwECanjiS0ANj+48a4gVIKCaCU5Adbd+BFSz+NLa3QoQUMMTXwJqeHTnWUOsAAHVTHACKvUzU8DMmvlH/cwUMLO2Pf/MRmevNQHV3tjQsyAqQEA1E9P2D2j6x/iaKWBmzfyjfmYK0NqbAgRU5kVEKEBANQszL8Du1q/WkCRmA6Q1FfBTgXqlekmzcgPUip8vfooXIYcTUCMk0JE+TAKqWQbwAuJu/QioZvGltf8KEFD91yzSLAiokRbxCB0vAdUs8ARUd+tHQDWLL639V4CA6r9mkWZBQI20iEfoeAmoZoEnoLpbPwKqWXxp7b8CBFT/NYs0C6sBdd++fbo3+2OPPeZXXA4dOiTHjh2TMmXKeLVbvXq1lCtXTpIkSTzrrrBX/YULF6RAgQJ+aWFy8NGjR+XixYtSsGBBv7r5448/1C4Uvp47d07Sp0+veRJfI6D6FcJ7Diaguls/AqpZfGntvwIEVP81izSLBAPU7du3y6hRo2T48OEBa/rpp59K6tSppWPHjj73AagqVKiQfPXVV1KtWjWvdgCa06dPS8qUKX3uN9wHTps2TdatWyfDhg0LmSuTJk2SHTt2yKBBg+46Z5cuXaRZs2ZStGhRr778+OOP+gWhfv36Cebr4cOHpWHDhpIlSxZJnjy5FCtWTN599904z0dANQsFAdXd+hFQzeJLa/8VIKD6r1mkWcQJqACiNWvWyPr16yU6OlpGjhwpVapU0f+3adNGDhw4IK+99poMHTpUq2X/+te/5LvvvpPnn39eADYVKlSQrVu3ysCBA+XVV1+VFi1ayKZNm6RGjRoyevRoOXPmjLRq1UpWrVql1cwJEyZI9uzZBXDz+uuvK3QAPl588UXp0KGDfPzxx+pDrly55IsvvlAg8dbQ19q1a6Vv377y3nvvCWBq3rx58vjjjwuA97nnntOKGwB1165d8sYbbwjGev36dWnbtq3s3btXmjZtKh9++KH6YEvzBNRFixZJ9+7d5ebNmzrGV155RWEcGgEaofOAAQNk9uzZXnWrVKmSlC5dWsaNGyfFixeXmTNnyq1bt+6J4ddffy1TpkyRK1euCMB/8ODBKkejRo00Dlu2bJGffvrpHl/++9//yp49e6RevXrSqVMnSZMmjSxfvlz9/Oyzz+KUFHk2depUyZ8/v+ZafA3xuXHjhvTv31/++usvPQegOGfOnF7NCKhmmUxAdbd+BFSz+NLafwUIqP5rFmkWcQLqkCFDBBd1AOPOnTsV+DZu3KgwCdDA9Dmg8/PPP5dt27bpD6CoR48eCqfZsmWTfv36yffff69gCMgBLAJK27dvr30dOXJEevXqpX1jGhn/fuqppxRQS5QoIbVq1dKqGPpr3LixVkWXLFmikINp/KRJk94TL/j71ltvyYoVKxR+Uf2DHeAZIBYVFaWACtB+4YUXZOLEiVK5cmWFKcA1/g0ABxQDrG1pDqAiLg8//LCOBWCGKuKvv/4q//nPfyRjxozSp08f1RNT3rVr1/aqW44cORTCUZnGuKHx8ePH74khABAxRuV29+7d+mUD53rppZcUZgGRANXYvuCLDb6cAP7z5cun+mOZRqlSpeTEiROSNWvWu2RF36i2jx8/Xn1GfgGyPdtDDz2kueO0q1ev6hINVNgXLFiguYUvF3Et2yCgmmUyAdXd+hFQzeJLa/8VIKD6r1mkWcQLqKiaAYjQ8ubNK6iMVaxYUSEDDVVPTAED6ho0aKCVtJo1a8ozzzyjANizZ09ZtmyZVummT5+uaxJPnjyplS8c57wGQIH9t99+q8egkgb4BKhiuv7SpUtaFa1Tp46eF6CCKd5HHnnknnj99ttvCsCAJFQYAcKAKgAr1rTi/wDUDBkyyD//+U+t5gJ20qZNqxVaTPvDZxyD/9vSHEAFkAKce/fura6hmtytWzd59NFHVS9UNaHh/PnzFdy86Qb4hw6AS3wpQNXxiSeeuCeGqISjioqf8+fPKwDfuXNHq+D4IoIYefMF4OwAatWqVVV3NAAq9HaWBuCLD3yHz/hC0Lx5c/1ig+Uhc+fOvQdQAbyeDVVvVI3xhQU+/uMf/4gzXARUs0wmoLpbPwKqWXxp7b8CBFT/NYs0i3gBFWCIihwaYBDVUkwpAyDQMEWMf2OaH1AHSJg8ebKCK4AJgLp06VKFQUynA4RQSQWgoj+AC6bx8be6devKjBkzdN0igBXtnXfe0WohQAY33qCS6jTATKZMmeIFVFRLAbOoqAI44ZcDqHjt3//+twBoUW0ErDkwjk6ffPJJqV69ujX54ABq2bJlNSaAcKdhyh6VZ+iL41DdRFwAdN50w9igOfRDtRXVSVRTY8cQ8XfWoF6+fFnSpUt3F6Cib2++4AuBA6iIKwAUDdV3rEl2ABVLFQC7nTt3Vl8ByWiAYVTXPRsqpc7f8TogGF9q8IUCfaIq7DR8OQGYx26AalsbAdAsMtTPTD8Cqpl+tPZfAQKq/5pFmkW8gArg/OGHHxTiAI4ARYAALv4AOgAC1m1u2LBBq6SoiqKiB6jB+lRMuwIWcEMNIApQiallVEJR3UPlFRU4VMFwNzamkNH/ypUrJXfu3LpOsnXr1rp+9Msvv1SAxTpDgCOmgL3d5ORZQQWoAb4A1W+//baOxQFUrEHF61hLiynoZ599VtfOFilSRKELsIeKpC3NAVQZwRyoAAAgAElEQVQAJXxELADWAMAPPvhA9e/atatWTtu1a6fVbAC6N93w5SA2oEKP2DEsWbJknICK2EIzb75guYcvgAptT506pbrj5q+nn35a3nzzTa3M4guEZ0NVeOHChTEvjRkzRsfn+Vp8sWIF1SyTCYDu1o+AahZfWvuvAAHVf80izSJeQEVlFFPfeMTR2LFjdb0jgAfTy5hezZMnj64Jdab5MbUP0MSNNQBMgCWmkLFWERU/9IO1posXL9Zpd6z1PHv2rPaFm2hQBcSaUIAlbnpBHwDYli1bKtwCLnGhHDFihIKMt+YJqFiP+vLLL8f48s0338icOXN03SVu0kKFEOcALOMRToAuNFTrsH41c+bM1uQDABXLJlDFfv/99zUeqDRiPShuLMLay59//lnX0aIyjdigWu1NN0yjO4CKdcKAeEy/4wuDZwwBmd4qqLgxCWteAcmIRWxfUEX3FVAdgbEcA3mB/vCFBj/xNayNBdh6tt9//13XvHprBFSzVCaguls/AqpZfGntvwIEVP81izSLeAEV06ioIqZKlUpB1WkASqxDRRXVuSkFMIQpX6xVdV4DdNy+fTum0om1pVjH6NlwsxOm8T3vmMdTAWAbewofN/LAF7wOAAKQxG646x9w6zT4hXH87W9/U2DDeeJ6vBTOibWtuKHH2w1YNiUHgBqAHddd656+euoW3xi8xTCu43F+LJtA88cXXzREHIL9BAUCqi/Kx30MAdXd+hFQzeJLa/8VIKD6r1mkWdwXUFEBtbEBbFGRjd3w+KnE9HxTG7V1o08EVLOoElDdrR8B1Sy+tPZfAQKq/5pFmkWcgIo729E8q5GRJg7H6x4FCKhmsSSguls/AqpZfGntvwIEVP81izSLBNtJKtKE5HjtVoCAahYfAqq79SOgmsWX1v4rQED1X7NIsyCgRlrEI3S8BFSzwBNQ3a0fAdUsvrT2XwECqv+aRZoFATXSIh6h4yWgmgWegOpu/QioZvGltf8KEFD91yzSLAiokRbxCB0vAdUs8ARU6memgJk184/6mSlgZm17/pmNzl5rAqq9saFnQVSAgGompu0f0PSP8TVTwMya+Uf9zBSgtTcFCKjMi4hQgIBqFmZegD30O7FRZEoJM0Ets95f5//f3MPWxvwziwz1c7d+ZqOz15qAam9s6FkQFSCgmonJCxwB1SyDzKyZf9TPTAEza9vzz2x09loTUO2NDT0LogIEVDMxbf+ADql/rKCaJVMA1iGNL/0LQAEzE8bXTD+3WhNQ3RpZjusuBQioZgnBCwgrqGYZZGbN/KN+ZgqYWduef2ajs9eagJoAsblz546sWLFCnn32WcmcObOeAduyYs/63Llzy+bNm6V06dJ+nRn9lStXTlKkSOGXnW0HX7lyRbZu3er3+E3HQUA1U9D2D+iQ+scKqlkyBWAd0vjSvwAUMDNhfM30c6s1ATUBIrt27VqFyb59+0q/fv30DNOmTZN169bJ22+/LVWqVJHdu3ff98xTp06VGzduSIsWLSRbtmyya9cuyZQp033tbD7g5MmTMnbsWOndu3dI3SSgmsnNCwgrqGYZZGbN/KN+ZgqYWduef2ajs9eagJoAsWnXrp1WSkeOHCmHDh2SpEmT3hdQJ06cKH369FFvALUVKlSQsmXLytWrV2XevHnStGlTqVOnjv67ZMmSMmPGDHnwwQdl0aJF0r17d7l586a899578sorr8j777+vYDt9+nQF4eTJk2u/qOICdjdt2iQ1atSQ0aNHy5kzZ6RVq1ayatUqheoJEybI9evXpX379pIhQwZZv369dOrUSZYsWSIbNmyQAQMGSOvWraVLly5y69YtiYqKkhw5csiUKVPk8ccfl3HjxsnQoUP1nJ07d5bmzZvf5c/SpUulV69e6v/MmTPl448/lkuXLsnAgQOlfv36er42bdrIgQMH5LXXXtO+cI5ly5bJL7/8IkePHpVBgwZJ48aNvdrHFU4Cqlmi2/4BHVL/WEE1S6YArEMaX/oXgAJmJoyvmX5utSagBjmyly9f1se1HDx4UIHv888/19/xVVABaHnz5pVZs2YJ7Fu2bCl79+5VOLt27ZoCa65cuaR27drStWtXefXVV6VHjx4KmQ8//LCC2l9//SUNGzaUX3/9VQEOQAdARrXWaf379xdMsQMuAaWA0I0bN8qRI0cUGlHxLViwoDRo0EDy5csngObUqVMrDI4ZM0Z9aNu2rR6Pc6ONHz9eRowYoX727NlTwRwgCygvXry4nDp1Sjp27BjjD7SpVauWwiaqwtu2bZMTJ04oOG/fvl21AmSXKVNGX4N+0AKQvHr1arUbMmRInPYE1CAn9P+64wWEFdSEySzfemX++aZTXEdRP3frZzY6e60JqEGODUAUYIgp7C+//FIyZsyo/48PUFEFBWSiCokGAEU18ffff1fwRF+AuW+++UaKFSsm77zzjsJi0aJF9VhnuvyLL76Qbt26yfLly/U4AKhnAzCiqlqgQAHBVDuqrDVr1ox5DdVWwOncuXPV/uzZs1phzZ49ux6Ln7Rp0wrW2AJQUUkFNB87dkzKly+vIIkfLHEAeAIkz507pxVexx/8HYD622+/yXPPPad9N2vWTKpVq6b9A5ABrGio5u7YsUMKFy6syyOg48WLFyV9+vRy+/bte+xRUSagBjmhCaj3CsoKasIkWTy9ErDMJKd+7tbPbHT2WhNQgxybSpUqSZo0aXR6HBAF+ARwYWo+rjWoo0aNUhgF0KFhOr9evXqCDxVPQHXWoGIqP2vWrPqDZQGeIIrzo6JZtWpVqVu3bszoAJXwCX3kzJlTK6kAwkceeUT27dsnWbJk0b/BZsGCBfp7y5YtWgEFIGIMWG7gCahYT1uxYkWdjscNYfAX/aC6W6JECYVsB1AdfxxARbUUkIyq8ezZs7V//Ea1Fn9Dw3IB/Bt9A1RRGYbfDzzwgEJybPudO3dq5XbNmjUKybEb/LK18QJiFpmQ6kdANQtWANYhjS/9C0ABMxPG10w/t1oTUIMYWUzrP/PMM1p5dNZ9YsoaU+qAu7gAdc+ePVqJxN9RISxVqpTe6T9p0iRdn4mpd8+bpBxAbdKkiRQpUkQAZsmSJVOo/OCDD3RtaWxAxTBRqQTAYl2os6YVMFq5cmWtxGI9KIAS0/i+ACrWwsI3VG4Bty+88IIuS8ATBxYuXKiVUmgBMIwNqKjywp/FixdLkiRJ9OYvjAOVVsAlKsSo5sKX06dP3wOox48fv8fegW9vIeUaVLNE5wXEQz8CqlkyBWDN/AtANA8T6udu/cxGZ681ATWIsQHg4SYeAJvTUBVFRQ/AhxuAAKve7uLHDUWYfgfIAiCHDRsmuKEIN0bNnz9fAKOeFVQAa4cOHfQGJNwVf/78eXnppZcEd/7jJi1MmcPWs2F9KqqtuFkKU+mAQ7yGm5IAkrg5CuCYKlUqnwAVQAkQj46OlpUrV+rNUgBMPForZcqUOhZAKjRx/PGsoAJQv//+e123iuUKAGssi8CSBfiC9aq4OWvOnDleK6je7OMKJwHVLNF5gSOgmmWQmTXzj/qZKWBmbXv+mY3OXmsCqkWxQfUSywKcZ6fCNUAeYA8V0rganq+KSium7n1pAEqsjfVseNoAbrhyKr/36wdrULEWFjd3YamB4x/8R3UTvuDJAliikC5duji7w3mx9MDTH8AppvxRRUV1Nb7mzd7b8QTU+0U0/r/b/gEdUv9YQTVLpgCsQxpf+heAAmYmjK+Zfm61JqC6NbIJPC4HULGEITE0AqpZlHgB8dCPgGqWTAFYM/8CEM3DhPq5Wz+z0dlrTUC1NzZWe4a1srgZzNeKa7gHQ0A1iwAvcARUswwys2b+UT8zBcysbc8/s9HZa01AtTc29CyIChBQzcS0/QM6pP6xgmqWTAFYhzS+9C8ABcxMGF8z/dxqTUB1a2Q5rrsUIKCaJQQvIKygmmWQmTXzj/qZKWBmbXv+mY3OXmsCqr2xoWdBVICAaiam7R/QIfWPFVSzZArAOqTxpX8BKGBmwvia6edWawKqWyPLcbGCGsQc4AXETEzqR/3MFDCzZv65Wz+z0dlrTUC1Nzb0LIgKREVFSaFChYLYI7uiAlSAClCBSFEAz+VmC60CBNTQ6s2zUQEqQAWoABWgAlSACtxHAQIqU4QKUAEqQAWoABWgAlTAKgUIqFaFg85QASpABagAFaACVIAKEFCZA1SAClABKkAFqAAVoAJWKUBAtSocdIYKUAEqQAWoABWgAlSAgMocoAJUgApQASpABagAFbBKAQKqVeGgM1SAClABKkAFqAAVoAIEVOYAFaACVIAKUAEqQAWogFUKEFCtCgedoQJUgApQASpABagAFSCgMgeoABWgAlSAClABKkAFrFKAgGpVOOgMFaACVIAKUAEqQAWoAAGVOUAFqAAVoAJUgApQASpglQIEVKvCQWeoABWgAlSAClABKkAFCKjMASpABagAFaACVIAKUAGrFCCgWhUOOkMFqAAVoAJUgApQASpAQGUOUAEqQAWoABWgAlSAClilAAHVqnDQGSpABagAFaACVIAKUAECKnOAClABKkAFqAAVoAJUwCoFCKhWhYPOUAEqQAWoABWgAlSAChBQmQNUgApQASpABagAFaACVilAQLUqHHSGClABKkAFqAAVoAJUgIDKHIgIBaKioqRQoUIRMVYOkgpQASpABYKrQJ48eYLbIXu7rwIE1PtKxAPcoMAnn3wiPXr0sHYo+/fvF5s/AOmfWerYrt+Eb9+SjBkzmQ0yAa2jo8/RPwN9Q6VfndJ9A/LS9veH7f4FJHoiMCKgJoIg0UVzBQioZhra/gFN/8zi2+zzJGYd0DriFUibKqOMbn8uIB34/g1INtcbEVBdH2IOEAoQUM3ygBcQd+tHQDWLL61FCKjMgmArQEANtqLsz0oFCKhmYSGguls/AqpZfGlNQGUOBF8BAmrwNWWPFipAQDULCgHV3foRUM3iS2sCKnMg+AoQUIOvKXu0UAECqllQCKju1o+AahZfWhNQmQPBV4CAGnxN2aOFChBQzYJCQHW3fgRUs/jSmoDKHAi+AgTU4GvKHi1UgIBqFhQCqrv1I6CaxZfWBFTmQPAVIKAGX1P2aKECBFSzoBBQ3a0fAdUsvrQmoDIHgq8AATX4mlrf49atW+X06dN3+Zk9e/ZEtdPS0aNH5eLFi1KwYEGf9Cag+iRTnAcRUN2tHwHVLL60JqAyB4KvAAE1+Jpa32O9evXk9u3b8vTTT8f4WqRIEXn55ZeNfJ86darcuHFDWrRoYdSPY1yuXDlZuXKlJEuW7J7+Jk2aJDt27JBBgwb5dC4Cqk8yEVDNZEq0+hFQEyjwEdQtn4MaQcEO0VAJqCES2qbTAFAbNWok+O3ZAJgLFy6UWbNmyZIlS2TEiBEyf/58mThxogwdOlSSJ08unTt3lubNm8uff/6pILpp0yapUaOGdO/eXcqXLy9Xr16VefPmSeXKlbXrw4cPS/v27SVDhgyyfv166dSpk/a9YcMGGTBggLRu3VrWrl0rPXv2lAMHDkj9+vVlyJAh0rVrV/nss8+kVq1aMnv2bOnSpYtMnjxZzzF8+HAF1ylTpsiVK1cE1dTBgwdLw4YN45SZgGqWgayguls/AqpZfGnNCipzIPgKEFCDr6n1PQJML1y4IAUKFIjxtV27dvLEE0/IU089Jb169ZKPPvpIhg0bJmXKlJHcuXMrUCZNmlSKFy8up06dkoEDByocAhxbtWolbdu2lc2bN8u1a9ekX79+kiJFCu177969ki9fPoXc1KlTS+PGjWXMmDGSK1cutTly5IiUKlVKARdQW6FCBRk7dqz6kS5dOjlz5oz2iz7nzJkjI0eOlMuXL+tyBADyunXrZPfu3erP9u3bCagJlH0EVDNhbdePgGoWX1oTUJkDwVeAgBp8Ta3vEYCaOXNmKVmyZIyvVapUkUcffVSWL18ulSpVkgYNGmgl9c6dOwqZqHJu27ZNq5vnzp3TY6ZPn66Qe/LkSZ3anzlzpvz111/Su3fvmH5hW6xYMTl79qzCJta64lj8pE2bVvvHetgVK1YoaGLKfsaMGfLiiy9KkiRJ9Lh3331X8ufPLy1btlQoPnTokILp119/rT/nz5+XjBkzal9xNVZQzdLSdsCif2bxJaCa6UdrAipzIPgKEFCDr6n1PcY1xQ/Hf/zxRylbtqxO46PqCXjMkiWL9OjRQ0qUKKFT8IBNVFV37dolOXPmVGi8efOmVj69AWrdunVly5YtWnktXLiwnDhxQpcCAFCxFhb9onpbvXp1mTBhgp7LE1CbNGmi58UPzhMdHS1RUVExa1BRUUW11QHUNWvWKFDHbujX1kbAMosM9TPTj4Bqph+tCajMgeArQEANvqbW9whABewBHJ2GauWtW7ekaNGi0qFDB4XEBQsW6J3yWH+KCifWp2JNKAAVa0lRRQXINm3aVOrUqSMHDx6US5cuSd++fe+qoMYHqMePH9eqKiAX/WLqH/DpACpAdtq0aQrOWBoAgEUlF1VZ5yap2IDqLQCsoJqlJQHQ3foRUM3iS2sCKnMg+AoQUIOvqfU9YvoeNx55NtzBj7WguIFp6dKlOn3fp08f+emnn+T555/XJQEpU6bUyicgFTdG4eYn3CyFRz0tXrxYfvjhBwVV3FgFeEXDFH98gIqqJ4B5586dejzWqwJUV61aJbiLH5VR3Lz1xhtvqC+oui5atEhQJSWghi7VCKhmWtuuHwHVLL60JqAyB4KvAAE1+Jq6rkdMw6PSiel8TLFjGh/giIbpdqz/dBoAFiDr7dFQ8QkD0M2aNavaYY1rpkyZ9FzXr19XKPV2Ln+EZgXVH7XuPdZ2wKJ/ZvEloJrpR2sCKnMg+AoQUIOvKXu0UAECqllQCIDu1o+AahZfWhNQmQPBV4CAGnxN2aOFChBQzYJCQHW3fgRUs/jSmoDKHAi+AgTU4GvKHi1UgIBqFhQCqrv1I6CaxZfWBFTmQPAVIKAGX1P2aKECBFSzoBBQ3a0fAdUsvrQmoDIHgq8AATX4mrJHCxUgoJoFhYDqbv0IqGbxpTUBlTkQfAUIqMHXlD1aqAAB1SwoBFR360dANYsvrQmozIHgK5BggIoLGlqePHnu8hr7pWNnIjxSyFs7evSoPhwez9YMtOGZmnhuZ1znCLRf2iVeBQioZrEjoLpbPwKqWXxpTUBlDgRfgQQDVOzZjudndu3a9S6vJ02aJM8++6zuWOTZ8FD2lStXypQpU2IewB7ocNu2bStVq1a9a6ekQPuinTsUIKCaxZGA6m79CKhm8aU1AZU5EHwFEhRQsSPR7t271ethw4bp9pX9+/eXChUq6E5BN27c0B2LsDMRttPE79q1ayukYutLVFMHDx4sDRs29DpybIGJHYXWr1+vD4wfOXKkVKlSRRxARV9dunSRefPmyeOPPy6ffvqpPPfcc/Lzzz9Lq1at9IHzHTt2lLfeekv27Nmjdtj5CFt3fvjhh5I8efLgK84ew6IAAdVMdgKqu/UjoJrFl9YEVOZA8BVIUEAFFHzzzTeyZcsWBU1M72PvdoAjtsZctmyZQmXZsmV1Z6IzZ87ofu8tWrSQdevWKdwOHDhQ7bw1VGlxDuzTjml97AG/cePGGEDFDkSDBg2Sr776Sr777juZOXOm7vOOau3bb78tzzzzjPz9739XG2yliS09K1euLG3atNH96F999dXgK84ew6IAAdVMdgKqu/Wb8O1bkjFjJrNBJqB1dPQ5+megb6j0q1O6b0Be8vMlINlcb5SggHrgwAEZPny4ipgtWzbZtGmT9OrVKwZQixUrpvu5oyVJkkQrqth3/euvv9af8+fP6zaa2K89LkBFlRWgipY3b15ZvXq19OvXT6f4UZEFfP7666+yYsUK2bdvn+4z/+STT8qJEyfUBq8BjrNnz66wjG06Ac7p06fX/7O5QwECqlkceQGhfmYKmFkz/6ifmQJm1rbnn9no7LVOUEDFFDqAFO2RRx7RqfXevXvHAKrnOlFPQN2xY4dWPi9fvqzwGB+gXrp0Sfr06RNzjkWLFsmIESMUUFOlSqVT+ZjCB3BOmDBBsAa2Xr16MUsPsO87lgfgZi4HdNEZILZ69er2Ro6e+aUAAdUvue452PYPaPrH+JopYGbN/KN+ZgrQ2psCCQqoqIL+8MMPsm3bNmnQoIFCoecUf2xAvXr1qk7D+wOozjl+++03qV+/vuzatUvatWungIp1rg899JC89957OqUPXzZs2CA5cuTQtauPPvqoFCpUSI/75z//qfBapEgRBd6cOXPK66+/zqxxiQIEVLNA8gIsItH7RbZPCUhIfBHOlMneKfT9OZrd88SVgAaaQEbMPzNhqZ+79TMbnb3WCQaouOlp1KhRcvPmTcGbY/Lkybqm0xNQq1WrJnXq1FF1sC4U1dJGjRr5Bag4D9aa/vnnnzJ27Fi9oQo3O6FvTNu//PLLkjt3br1JCuth58yZI2fPntVK7vXr19WfDz74QBYsWKA3VKGlTp1aoRWPqmJzhwIEVLM48gL3P0Adm9dMSEut99fdR0A1iA3fHwbiiSgjxH4kpVmPwbW23b/gjtae3hIMUJ0hYh1psmTJFD7jawBZACNgM3bbunWr/P777/e8jvWjt27d0konpvO92eJpAFhq8Le//U2fDIA787HOFOfCOT1t8P/Dhw/LY489JkmTJrUnSvTEWAECqpmEtn9Ah8Q/VFAJqGaJFKB1SOIboG8wo38G4lE/M/FcbJ3ggBoM7Q4dOqQV0tgNN0ThxipnnWswzsU+3KkAAdUsrrwAs4JqlkFm1sw/6memgJm17flnNjp7rRMFoMYlH9asoqVJk8ZehemZFQoQUM3CYPsHdEj8YwXVLIkMrEMSX/pnoICZKeNrpp9brRM1oLo1KBxX8BUgoJppygsIK6hmGWRmzfyjfmYKmFnbnn9mo7PXmoBqb2zoWRAVIKCaiWn7B3RI/GMF1SyJDKxDEl/6Z6CAmSnja6afW60JqG6NLMd1lwIEVLOE4AWEFVSzDDKzZv5RPzMFzKxtzz+z0dlrTUC1Nzb0LIgKEFDNxLT9Azok/rGCapZEBtYhiS/9M1DAzJTxNdPPrdYE1ASILHa+wtaqzz77bMyzVPEUggsXLugzWTdv3iylS5f268zoD8+KTZEihV92th2MR33hsWH+jt90HARUMwV5AWEF1SyDzKyZf9TPTAEza9vzz2x09loTUBMgNmvXrlWY7Nu3r/Tr10/PMG3aNFm3bp3uaFWlSpWYrVbjO/3UqVP1MVotWrSQbNmy6S5ZNu9G44uUJ0+e1A0VsFFCKBsB1Uxt2z+gQ+IfK6hmSWRgHZL40j8DBcxMGV8z/dxqTUBNgMhiq1VUSkeOHCl4hise+n8/QJ04caJusYoGqK1QoYKULVtW8CitefPmSdOmTXXXLfy7ZMmSMmPGDHnwwQdl0aJF0r17d910AFu6vvLKK/L+++8r2E6fPl1BGJsToKGKC9jdtGmT1KhRQ0aPHi1nzpyRVq1a6c5ZgOoJEyboJgbt27eXDBkyyPr166VTp06yZMkS3SZ2wIAB0rp1a911C5skREVF6daxU6ZM0d26xo0bJ9jdC+fs3Lmz7tTl6c/SpUv1ubXwH9vafvzxx3Lp0iUZOHCgblWL87Vp00YOHDggr732mvaFcyxbtkx++eUXOXr0qAwaNEgaN27s1T6ucBJQzRKdFxBWUM0yyMya+Uf9zBQws7Y9/8xGZ681ATXIsbl8+bJu2Xbw4EEFvs8//1x/xweoALS8efPKrFmzBPYtW7aUvXv3Kpxdu3ZNgTVXrlxSu3Zt6dq1q24Z26NHD4XMhx9+WEENu2Vhm9dff/1VAQ5AB0BGtdZp/fv31920AJeAUkDoxo0b5ciRIwqNqPgWLFhQGjRoIPny5RNAM7Z9BQyOGTNGfcA2sjge50YbP368jBgxQv3s2bOngjlAFlBevHhxOXXqlHTs2DHGH2hTq1YthU1Uhbdt2yYnTpxQcN6+fbtqBcguU6aMvgb9oAUgGRszwG7IkCFx2hNQg5zQ/+vO9g/okPjHCmrCJJcPvYYkvj74Edch9M9APO4kZSaei60JqEEOLkAUYIgp7C+//FIyZsyo/48PUFEFBWSiCokGAEU1Edu7AjzRF2Dum2++kWLFisk777yjsFi0aFE91pku/+KLL6Rbt26yfPlyPQ4A6tkAjKiqFihQQDDVjiprzZo1Y15DtRVwOnfuXLU/e/asVlizZ8+ux+IHW8NijS0AFZVUQPOxY8ekfPnyCpL4wRIHgCdA8ty5c1rhdfzB3wGov/32mzz33HPad7NmzaRatWraPwAZwIqGau6OHTukcOHCujwCOl68eFHSp08vt2/fvsceFWUCapATmoD6f4ISUBMmuXzolQDog0jxHEL93K2f2ejstSagBjk2lSpV0p2tMD0OiAJ8ArgwNR/XGtRRo0YpjALo0DCdX69ePd3f2RNQnTWomMrPmjWr/mBZgCeI4vyoaFatWlXq1q0bMzpAJXxCHzlz5tRKKoDwkUcekX379kmWLFn0b7BZsGCB/t6yZYtWQAGIGAOWG3gCKtbTVqxYUafjcUMY/EU/qO6WKFFCIdsBVMcfB1BRLQUko2o8e/Zs7R+/Ua3F39CwXAD/Rt8AVVSG4fcDDzygkBzbfufOnVq5XbNmjUJy7Aa/bG28gJhFJiT6EVDNgmRgHZL40j8DBcxMGV8z/dxqTUANYmQxrf/MM89o5dFZ94kpa0ypA+7iAtQ9e/ZoJRJ/R4WwVKlSeqf/pEmTdH0mpt49b5JyALVJkyZSpEgRAZglS5ZMofKDDz7QtaWxARXDRKUSAIt1oc6aVsBo5cqVtRKL9aAASkzj+wKoWAsL31C5Bdy+8MILuiwBTxxYuHChVkqhBcAwNqCiygt/Fi9eLEmSJNGbvzAOVFoBl6gQo5oLX06fPp/cYecAACAASURBVH0PoB4/fvweewe+vYWUa1DNEp0XEK5BNcsgM2vmH/UzU8DM2vb8MxudvdYE1CDGBoCHm3gAbE5DVRQVPQAfbgACrHq7ix83FGH6HSALgBw2bJjghiLcGDV//nwBjHpWUAGsHTp00BuQcFf8+fPn5aWXXhLc+Y+btDBlDlvPhvWpqLbiZilMpQMO8RpuSgJI4uYogGOqVKl8AlQAJUA8OjpaVq5cqTdLATAzZ84sKVOm1LEAUqGJ449nBRWA+v333+u6VSxXAFhjWQSWLMAXrFfFzVlz5szxWkH1Zh9XOAmoZolu+wd0SPxjBdUsiQysQxJf+meggJkp42umn1utCagWRRbVSywLAOA5DZAH2EOFNK6G56ui0oqpe18agBJrYz0bnjaAG66cyu/9+sEaVKyFxc1dWGrg+Af/Ud2EL3iyAJYopEuXLs7ucF4sPfD0B3CKKX9UUVFdja95s/d2PAH1fhGN/++8gLCCapZBZtbMP+pnpoCZte35ZzY6e60JqPbGxmrPHEDFEobE0AioZlGy/QM6JP6xgmqWRAbWIYkv/TNQwMyU8TXTz63WBFS3RjaBx4W1srgZzNeKawK7c9/uCaj3lSjeA3gBYQXVLIPMrJl/1M9MATNr2/PPbHT2WhNQ7Y0NPQuiAgRUMzFt/4AOiX+soJolkYF1SOJL/wwUMDNlfM30c6s1AdWtkeW47lKAgGqWELyAsIJqlkFm1sw/6memgJm17flnNjp7rQmo9saGngVRAQKqmZi2f0CHxD9WUM2SyMA6JPGlfwYKmJkyvmb6udWagOrWyHJcrKAGMQd4AWEFNYjp5HdXzD+/JbvLgPq5Wz+z0dlrTUC1Nzb0LIgKsIJqJiYvcARUswwys2b+UT8zBcysbc8/s9HZa01AtTc29CyIChBQzcS0/QM6JP5xit8siQysQxJf+meggJkp42umn1utCahujSzHdZcCBFSzhOAFhBVUswwys2b+UT8zBcysbc8/s9HZa01AtTc29CyIChBQzcS0/QM6JP6hgrp9SkBCYpe4TJkyBWQbCqP9OZrp1sK2tpDE12Dw9M9APBGhfmb6udWagOrWyHJcrKAGMQd4ATETk/pRPzMFzKyZf+7Wz2x09loTUO2NDT0LogJRUVFSqFChIPbIrqgAFaACVCBSFLB5hsGtMSCgujWyHBcVoAJUgApQASpABRKpAgTURBo4uk0FqAAVoAJUgApQAbcqQEB1a2Q5LipABagAFaACVIAKJFIFCKiJNHB0mwpQASpABagAFaACblWAgOrWyHJcVIAKUAEqQAWoABVIpAoQUBNp4Og2FaACVIAKUAEqQAXcqgAB1a2R5bioABWgAlSAClABKpBIFSCgJtLA0W0qQAWoABWgAlSACrhVAQKqWyPLcVEBKkAFqAAVoAJUIJEqQEBNpIGj21SAClABKkAFqAAVcKsCBFS3RpbjogJUgApQASpABahAIlWAgJpIA0e3qQAVoAJUgApQASrgVgUIqG6NLMdFBagAFaACVIAKUIFEqgABNZEGjm5TASpABagAFaACVMCtChBQ3RpZjosKUAEqQAWoABWgAolUAQJqIg0c3aYCVIAKUAEqQAWogFsVIKC6NbIcFxWgAlSAClABKkAFEqkCBNREGji6TQWoABWgAlSAClABtypAQHVrZDkuKkAFqAAVoAJUgAokUgUIqIk0cHSbClABKkAFqAAVoAJuVYCA6tbIclxUgApQASpABagAFUikChBQE2ng6LZ/CkRFRUmhQoX8M+LRVIAKUAEqQAVEJE+ePNQhxAoQUEMsOE8XHgU++eQT6dGjR3hO7sNZ9+/fb/UHIP3zIYjxHGK7fkuWvSWZMmUyG2QCWp87d86V/pUo1jcBVfu/rm3PP/oXkjRIdCchoCa6kNHhQBQgoAaiGi9wZqolHv1GjksSrKGyHx8VeDhHRan54nIfjzY7jADobv3MRmevNQHV3tjQsyAqQEA1E5MXOHfrR0A1i28g1gTUxPMFzvbPv0DyLzHYEFATQ5Too7ECBFQzCW3/gKZ/ZvEloJrpF4g1AZWAGkjeRJINATWSoh3BYyWgmgWfAOhu/QioZvENxJqASkANJG8iyYaAGknRjuCxElDNgk9Adbd+BFSz+AZiTUAloAaSN5FkQ0CNpGhH8FgJqGbBJ6C6Wz8Cqll8A7EmoBJQA8mbSLIhoEZStCN4rARUs+ATUN2tHwHVLL6BWBNQCaiB5E0k2RBQIynaETxWAqpZ8Amo7taPgGoW30CsCagE1EDyJpJsCKiRFO3/jXXr1q1y+vTpu0aePXv2RLXT0tGjR+XixYtSsGBBnyJIQPVJpjgPIqC6Wz8Cqll8A7EmoBJQA8mbSLIhoEZStP831nr16snt27fl6aefjhl9kSJF5OWXXzZSY+rUqXLjxg1p0aKFUT+Ocbly5WTlypWSLFmye/qbNGmS7NixQwYNGuTTuQioPslEQDWTKdHqR0BNoMDH0y0BlYAa+qxLXGckoCaueAXFWwBqo0aNBL89GwBz4cKFMmvWLFmyZImMGDFC5s+fLxMnTpShQ4dK8uTJpXPnztK8eXP5888/FUQ3bdokNWrUkO7du0v58uXl6tWrMm/ePKlcubJ2ffjwYWnfvr1kyJBB1q9fL506ddK+N2zYIAMGDJDWrVvL2rVrpWfPnnLgwAGpX7++DBkyRLp27SqfffaZ1KpVS2bPni1dunSRyZMn6zmGDx+u4DplyhS5cuWKoJo6ePBgadiwYZz6EFDNUocVVHfrR0A1i28g1gRUAmogeRNJNgTUSIq2RwX1woULUqBAgZjRt2vXTp544gl56qmnpFevXvLRRx/JsGHDpEyZMpI7d24FyqRJk0rx4sXl1KlTMnDgQIVDgGOrVq2kbdu2snnzZrl27Zr069dPUqRIoX3v3btX8uXLp5CbOnVqady4sYwZM0Zy5cqlNkeOHJFSpUop4AJqK1SoIGPHjlU/0qVLJ2fOnNF+0eecOXNk5MiRcvnyZV2OAEBet26d7N69W/3Zvn07ATWB8pmAaias7foRUM3iG4g1AZWAGkjeRJINATWSou0BqJkzZ5aSJUvGjL5KlSry6KOPyvLly6VSpUrSoEEDraTeuXNHIRNVzm3btml189y5c3rM9OnTFXJPnjypU/szZ86Uv/76S3r37h3TL2yLFSsmZ8+eVdjEWlcci5+0adNq/1gPu2LFCgVNTNnPmDFDXnzxRUmSJIke9+6770r+/PmlZcuWCsWHDh1SMP3666/15/z585IxY0btK67GCqpZotsOWPTPLL4EVDP9ArEmoBJQA8mbSLIhoEZStD0A1dsUP/78448/StmyZXUaH1VPwGOWLFmkR48eUqJECZ2CB2yiqrpr1y7JmTOnQuPNmze18ukNUOvWrStbtmzRymvhwoXlxIkTuhQAgIq1sOgX1dvq1avLhAkT9FyegNqkSRM9L35wnujoaImKiopZg4qKKqqtDqCuWbNGgTp2Q7+2NgKWWWSon5l+BFQz/QKxJqASUAPJm0iyIaBGUrQ9ABWwB3B0GqqVt27dkqJFi0qHDh0UEhcsWKB3ymP9KSqcWJ+KNaEAVKwlRRUVINu0aVOpU6eOHDx4UC5duiR9+/a9q4IaH6AeP35cq6qAXPSLqX/ApwOoANlp06YpOGNpAAAWlVxUZZ2bpGIDqreQsoJqlugEQHfrR0A1i28g1gRUAmogeRNJNgTUSIr2/8aK6XvceOTZcAc/1oLiBqalS5fq9H2fPn3kp59+kueff16wJCBlypRa+QSk4sYo3PyEm6XwqKfFixfLDz/8oKCKG6sAr2iY4o8PUFH1xM1aO3fu1OOxXhWgumrVKsFd/KiM4uatN954Q31B1XXRokWCKikBNXTJS0A109p2/QioZvENxJqASkANJG8iyYaAGknRDnCsmIZHpRPT+ZhixzQ+wBEN0+1Y/+k0ACxA1tujoeI7PUA3a9asaoc1rpkyZdJzXb9+XaHU27n8GQ4rqP6ode+xtgMW/TOLLwHVTL9ArAmoBNRA8iaSbAiokRTtCB4rAdUs+ARAd+tHQDWLbyDWBFQCaiB5E0k2BNRIinYEj5WAahZ8Aqq79SOgmsU3EGsCKgE1kLyJJBsCaiRFO4LHSkA1Cz4B1d36EVDN4huINQGVgBpI3kSSDQE1kqIdwWMloJoFn4Dqbv0IqGbxDcSagEpADSRvIsmGgBpJ0Y7gsRJQzYJPQHW3fgRUs/gGYk1AJaAGkjeRZENAjaRoR/BYCahmwSeguls/AqpZfAOxJqASUAPJm0iyiRhAxT7t2BEJjzJiizwFCKhmMSeguls/AqpZfAOxJqASUAPJm0iycTWgAkpHjRolw4cPl0mTJsmzzz6rOyWxRZ4CBFSzmBNQ3a0fAdUsvoFYE1AJqIHkTSTZJCigYs/1Vq1axewKhG0qsa0ltqx87733dD/3Dz/8UKpUqXLPa9ib/Z133pGvvvpK44EdikaMGCGDBw/WLTmxHWaOHDlkypQp8vjjj8u4ceN0S87kyZNL586ddQtOAOnWrVtl4MCB+nD5ChUq6O5E3bp1k/Hjx6vd6NGjpXTp0rrzEX6jn+LFi8vMmTMlffr0kZQLrh4rAdUsvARUd+tHQDWLbyDWBFQCaiB5E0k2CQqo/fv3lyNHjkivXr10f3ZsiYk93PPmzavQeuDAAd3zfe3atfe8BkBs1KiRbNmyReNRoEAB+e6773SfeDQAJoD12rVr0rNnT4XdDRs2SNKkSRUwT506JatXr5Z+/frJ999/r1tl1q5dW6ET4Pvll1/qlpnYonPZsmWSLVs23VO+Y8eOuvXmu+++K9j+k80dChBQzeJIQHW3fkuWvaW7t9nanN3l3OZfiWJ9QzIkvn/NZLZdP7PR2WudoIAKUMSe7oDL3bt3C/aA/+ijj2TWrFkKiGibNm2SY8eOaaXU8zVsl9mkSROvgNq6dWuFTdiVL19e93vHD0B327ZtMmTIEN0u8/fff1d4BYCiogob7OeeP39+reyiAUyxFOCpp56SjRs3Sq5cuRSosa2nA8P2ho+e+aoAAdVXpbwfZ/sHNP1jfM0UMLNm/lE/MwVo7U2BBAPUO3fuSIYMGWTfvn16c9KuXbukbt26Cn07duzQdaFo2ON93rx5snPnzrteO3v2rDRu3DgGUFH5xHQ97N9++22pWLGiVmAxjY8PB5wD1dgSJUpI/fr14wTUGTNmaL+AVTT0C7h9+umn1UdUEVDtfeihh7SayuYOBQioZnHkBZj6mSlgZs38o35mCphZ255/ZqOz1zrBABVDfvXVV6Vy5cr6++OPP1ZoROWyZs2aWq3E9D+m0+fOnSu1atW66zVM5xcqVEirpIcOHVKABJACUEuWLKkQ+cUXXyhUvvDCC7r+dMWKFbJw4ULtC4C7Z88e6dKli6xZsyamggofUGnF2tOff/5Zp/6xjACVVAKqvYlq6hkB1UxB2z+gjfxb1d9MHB+sz0Wfk0wZ7Z1C35+rmeTJk8eHkYTnEKP4hsBl+mcmMvUz08+t1gkKqOvXr5c2bdooLF6/fl2WL1+uU+lYAzpt2jTVFJVUVDO9vYb1qlgOgGn3o0ePyrp16xRQUW3FzVDR0dGycuVKvVmqWLFikjlzZsHSgKtXryqkvvnmm3ojFKbsAZ84T9myZRWSsbQAfWKta8OGDe8CVKxbRQWVU/zuSXsCqlksXX0BAaCu7mcmUCK33t94HwHVIIaufn8Y6OKrKfXzVanIOi5BAdWREhXQhx9+WKHSaRcvXpQ0adLc97ULFy7cdTd9jRo1FGZxoxWeaZosWTLt8vbt27pcAGtHb968qXftp0uXTv+NvwFcPdsff/whGTNmlLRp00ZWxCN0tARUs8C7+gJCQBUCKt8fZgqYWbv688VMmoi2DgmgBlNhB1DxuCg2KuCrAgRUX5XyfpyrLyAEVAKq2dtD74PgEonARaR+gWvnZstEB6jeKq9uDhDHFhwFCKhmOrr6AkJAJaCavT0IqNTPUAGae1Mg0QEqw0gFAlGAgBqIav9nQ0A10892a07xm0XI1e8PM2l8sqZ+PskUcQcRUCMu5JE5YAKqWdxdfQFhBZUVVLO3Byuo1M9QAZqzgsociFgFCKhmoSegmulnuzUrqGYRcvX7w0wan6ypn08yRdxBrKBGXMgjc8AEVLO4u/oCwgoqK6hmbw9WUKmfoQI0ZwU1RDmAXbSwaQB2ucKzWdH+/PNPwSOzcufOLZs3b5bSpUv75Q36w5MLUqRI4ZedbQdfuXJFdwTzd/ym4yCgmilIQDXTz3ZrVlDNIuTq94eZND5ZUz+fZIq4g1hBTYCQY6cqwCR2u8JD/9GwMQE2GsA2rVWqVJHdu3ff98xTp06VGzduSIsWLe7aSOC+hhYfcPLkSRk7dqz07t07pF4SUM3kdvUFhBVUVlDN3h6soFI/QwVozgpqiHKgXbt2WikdOXKkbtOaNGnS+wLqxIkTpU+fPuohoLZChQq66xV2xZo3b540bdpU6tSpo//GVq8zZsyQBx98UBYtWiTdu3fXDQnee+89eeWVV+T9999XsJ0+fbqCsLNBAqq4gF3sooXnyWK71zNnzujOWqtWrVKonjBhgu761b59e8mQIYNgNzDs6LVkyRLZsGGDDBgwQFq3bq1byN66dUuioqJ0J68pU6borl3jxo3TbWdxzs6dO+sWs57+LF26VHf2gv/YxQtb4F66dEkGDhwo9evX1/Nh9zFsa/vaa69pXzjHsmXL5JdfftHdvwYNGiSNGzf2ah9XiAmoZslPQDXTz3ZrVlDNIuTq94eZND5ZUz+fZIq4g1hBDXLIL1++rA9sPnjwoALf559/rr/jq6AC0LAzFrZ1hX3Lli1l7969CmfXrl1TYMV2r9iqtWvXrvLqq69Kjx49FDKxQxdADztnYcvWX3/9VQEOQAdARrXWaf379xdMsQMuAaWA0I0bN8qRI0cUGlHxLViwoDRo0EDy5csngObUqVMrDI4ZM0Z9aNu2rR6Pc6ONHz9eRowYoX727NlTwRwgCygvXry4nDp1Sjp27BjjD7TBNrSAzWzZssm2bdvkxIkTCs7bt29XrQDZZcqU0degH7QAJK9evVrthgwZEqc9ATXICf2/7lx9AWEFlRVUw7eNq98fhtr4Yk79fFEp8o4hoAY55gBRgCGmsL/88kvdThX/jw9QUQUFZKIKiQYARTXx999/V/BEX4C5b775RooVK6ZbvQIWixYtqsc60+VffPGFdOvWTZYvX67HAUA9G4ARVdUCBQoIptpRZa1Zs2bMa6i2Ak7nzp2r9mfPntUKa/bs2fVY/GBrWKyxBaCikgpoPnbsmJQvX15BEj9Y4gDwBEieO3dOK7yOP/g7APW3336T5557Tvtu1qyZVKtWTfsHIANY0VDN3bFjhxQuXFiXR0BHbNSQPn163b42tj0qygTUICc0ATVhBLWsV1ZQzQJCwKJ+ZgrQ2psCBNQg50WlSpUkTZo0Oj0OiAJ8ArgwNR/XGtRRo0YpjALo0DCdX69ePV3X5Amou3btkkyZMulUftasWfUHywI8QRTnR0WzatWqUrdu3ZjRASrhE/rImTOnVlIBhI888ojs27dPsmTJon+DzYIFC/T3li1btAIKQMQYsNzAE1CxnrZixYo6HY8bwuAv+kF1t0SJEgrZDqA6/jiAimopIBlV49mzZ2v/+I1qLf6GhuUC+Df6BqiiMgy/H3jgAYXk2PY7d+7Uyu2aNWsUkmM3+GVr4wXOLDJG+rGCygqqWfpxDSr1M1SA5gTUBM4BTOs/88wzWnl01n1iyhpT6oC7uAB1z549WonE31EhLFWqlN7pP2nSJF2fial3VFBjA2qTJk2kSJEiAjBLliyZQuUHH3yga0tjAyqGjkolABbrQp01rYDRypUrayUW60EBlJjG9wVQsRYWvqFyC99eeOEFXZaAJw4sXLhQK6XQAmAYG1BR5YU/ixcvliRJkih4YxyotAIuUSFGNRe+nD59+h5APX78+D32Dnx7CzPXoJolvxEAmp3aJ2sj/wioBFSfsizug4zyz/DcvpjTP19USrzxNRudvdasoAYxNgA83MQDYHMaqqKo6AH4cAMQYNXbXfy4oQjT7wBZAOSwYcMENxThxqj58+cLYNQTUAGsHTp00BuQcFf8+fPn5aWXXhLc+Y+btDBlDlvPhvWpqLbiZilMpQMO8RpuSgJI4uYogGOqVKl8AlQAJUA8OjpaVq5cqTdLATDxaK2UKVPqWACp0MTxx7OCCkD9/vvvdd0qlisArLEsAksW4AvWq+LmrDlz5nitoHqzjyucBFSzRHf1BY6ASkA1e3uwgkr9DBWgOSuolucAqpdYFuA8OxXuAvIAe6iQxtXwfFVUWjF170sDUGJtrGfD0wZww5VT+b1fP1iDirWwuLkLSw0c/+A/qpvwBU8WwBKFdOnSxdkdzoulB57+AE4x5Y8qKqqr8TVv9t6OJ6DeL6Lx/52Aaqaf7dZcg2oWIVe/P8yk8cma+vkkU8QdxApqxIU8OAN2ABVLGBJDI6CaRcnVFxBWUFlBNXt7sIJK/QwVoDkrqMyBoCmAtbK4GczXimvQThxgRwTUAIX7nxkB1Uw/261ZQTWLkKvfH2bS+GRN/XySKeIOYgU14kIemQMmoJrF3dUXEFZQWUE1e3uwgkr9DBWgOSuozIGIVYCAahZ6AqqZfrZbs4JqFiFXvz/MpPHJmvr5JFPEHcQKasSFPDIHTEA1i7urLyCsoLKCavb2YAWV+hkqQHNWUJkDEasAAdUs9ARUM/1st2YF1SxCrn5/mEnjkzX180mmiDuIFdSIC3lkDpiAahZ3V19AWEFlBdXs7cEKKvUzVIDmrKAyByJWAQKqWegJqGb62W7NCqpZhFz9/jCTxidr6ueTTBF3ECuoERfyyBwwAdUs7q6+gLCCygqq2duDFVTqZ6gAzVlBZQ5ErAIEVLPQux5QzeS5r/W56HOSKWOm+x4XrgP252qmWwvb2lydfyEQnfqZiWy7fmajs9eaFVR7Y0PPgqgAAdVMTNs/oOkf42umgJk184/6mSlAa1ZQmQMRq0BUVJQUKlQoYsfPgVMBKkAFqEDgCtg8wxD4qOy2ZAXV7vjQuyApMH36dDly5EiQemM3VIAKUAEqECkKPPLII9KkSZNIGa414ySgWhMKOpKQCtg+xU//zKJP/aifmQJm1sw/6memAK29KUBAZV5EhAK8gJiFmfpRPzMFzKyZf9TPTAEza9vzz2x09loTUO2NDT0LogK2f8DQP7NgUz/qZ6aAmTXzj/qZKUBrVlCZAxGrAC8gZqGnftTPTAEza+Yf9TNTwMza9vwzG5291qyg2hsbehZEBdasWSPlypULYo/B7Yr+melJ/aifmQJm1sw/6memAK1ZQWUOUAEqQAWoABWgAlSAClivACuo1oeIDlIBKkAFqAAVoAJUILIUIKBGVrw52ngUuHDhgqRPn/6uIy5fvixp0qSRpEmThky78+fPS4YMGUJ2Pn9PdObMGdUpRYoU/pqG7Pg//vhDcubMGbLz+XMi6Jc5c2Z/TBL82IsXL8qDDz6Y4OcJ9AQ2ahZ7LLbnnM3v2XB/5l26dEkeeOABSZIkSUxYw/HZH+j7w612BFS3Rpbj8lmBvXv3ysqVK2XIkCGyfft2tTt9+rQ+mDl58uRy6NAh6datmzRv3tznPgM5cP369fLRRx9JxowZ5eTJk9K/f38pWbJkIF0liA10aNq0qeCh1fCvY8eOUrt27QQ5l0mn2DXspZdektu3b991wTHpMxi2y5cvl9dff12KFSsmuPi1bt1aatasGYyuA+5j48aN0rJlS8mdO7fm+fjx46VEiRIB9xdsQxs18zZGW3PO9vdsuD/z8Dm2detWqVOnjuA6kDVr1rB89gf7feOW/giobokkxxGwAgMHDpRt27bJ5s2bYwAVr6GqNGDAADl+/LjkyJFDoSJt2rQBn+d+hi1atJBSpUopxOCu0aNHj8rw4cPvZxayvwPQq1evLg0bNhRUtPDBXrFixZCd35cTHTx4UHr37i3YOcw2QIVW8K1y5cqyatUqadOmjezevduXYSXYMVWqVJGuXbsKfs+dO1dGjx4t33//fYKdz9+ObdQs9hhszjnb37Ph/sxDzq9du1aGDh0qJ06cUEANx2e/v++LSDmegBopkeY441UAsNW4ceMYQEV1CyDRqFEjuXPnjk7x79u3TxJyP2ZUi1CRrFWrlixYsEBBoXTp0tZE7vnnn9fq6bfffit///vfZdCgQfLkk09a49+1a9fkn//8p0yePFn9tA1Q8YUHX3CSJUsmXbp0katXr8rIkSPDqh90+vHHH1WvTZs2SbVq1fRCbUuzUTNPbWzPOdvfs7Z85mFq3wHUcHz22/J+s80PAqptEaE/CaLAX3/95bUyVLVqVUmVKpVWAz0BtUGDBoKfevXqqT/ZsmWTdevWyWOPPWbs37Jly7Qa69kwrTpmzBiZPXu2ViinTZsmbdu2lbffftv4fP52EJd/RYsWlQoVKsinn36qld3o6GgZN26cv90bHx+Xf6g6FylSRFCVwQUnXIAal39YE4uL4Jtvvil79uyRhQsX6tR6OBvWJaKKixmC/fv3a3yPHDkSTpfuObdtmnk6+K9//cuKnIsrYPjcsuE9G5d//fr1s+IzzxNQE/Kz36o3ViJwhoCaCIJEF80VAEy1a9funo4AhbghKTagvv/++3oj0FtvvSW3bt2STJkyKZAF42YpVM+OHTt2ly94DesR58+fL2XKlBFUFrDG01kTa66A7z3E5R/WKg4ePFin+VFNhp/hqLZ58w/LIjAd7KzZ3bBhg/4bz6dMmTKl74MPwpFx6QcIhI+vvvqq9OjRQ1KnTh2Es5l1Ub58eZ3eLF68uPzyyy+CvAc429IOHz5snWaONviSmS5dOityLq54NNAKoAAADu5JREFUPfXUU1a8Z+MDaBs+8zwBNSE/+215XyUWPwioiSVS9DNBFYgNqLhIjxgxQquuqGriBqqffvopQX3A9DTgBcsKUEHFNP9XX32VoOf0p/NWrVppJRk3ck2dOlW+++47mTJlij9dJNixWIaBmxyclj9/ftm1a5fgt+eduQnmgA8do0IPaAbA2tJQoX/ooYeke/fuuhYVwPXBBx/Y4p7OatimmSNOYsg5m9+z0NGWzzxPQA3HZ781bzjLHCGgWhYQuhMeBWIDKtYH4sNzx44dulZwyZIl8txzzyWocz/88IM0a9ZM1yni6QFjx47VKqUtDVPAb7zxhlZ1c+XKJf/5z38E0/42tnBO8cdXLcJdw07DDRnhqEB7+udUwvEanh6BL2F/+9vfrAkpvhDZpllc4tiYc7a/Z235zEPskGdZsmTRz/tQf/Zb84azzBECqmUBoTt2KYD1eNmzZw/ZMz9RlcHd+7hpxdaGR3Ch6sbmDgVu3rwpeIYncs6WarM7lLVnFDa/Z239zAv1Z7892WKPJwRUe2JBT6gAFaACVIAKUAEqQAVEhIDKNKACVIAKUAEqQAWoABWwSgECqlXhoDNUgApQASpABagAFaACBFTmABWgAlSAClABKkAFqIBVChBQrQoHnaECVIAKUAEqQAWoABUgoDIHqAAVCJoCFy5ciNnYIGidBqkjPD4GLU2aNEHqkd0ES4EbN24IfvCINbc2vDeQg3h0lq8NO+Bt3rxZChYsqBuKxG5nz57Vl2x6NJivY+NxVOB+ChBQ76cQ/04FqMB9FcBD8rHT1OrVq/XYPHny6HNc//GPf9zXNlQHPPvss4ILPrb2TKgGCAFI4GH8n332WdBOU6pUKcG+71u2bAlan+ho0aJF2mevXr3i7Rfnx45c2JkrIRp21ho0aJAcP348XoCLz48vvvhCn+XatGnTgFwMhsbwYcKECTqGJk2a6A92n8OjlPAc5RdeeEEGDBjgs38zZ87UzQrwmKjMmTPfYzdx4kR555135MCBA66Ge58F44GuUoCA6qpwcjBUIPQK4Dma2CoTmx288sorkipVKhk/frw6gj3nn3jiidA75eWMw4cPF/jauXPnBPPn/PnzCkk4B3YfC1aDvoDrYG99i+1/sd3v7du3430GKuAecV2/fn2whnRXPwBlwG/v3r11N6u4Wnx+4DmuqDRi97dAmqnGK1as0C9k2MQCFU28H1auXCnYTha70WGPd+xuVqBAAZ/dq1Klim6zPGvWLK82Fy9e1C2ZP/74YwVVNirgJgUIqG6KJsdCBcKgwDfffKM7r7z00ksx+7hjm1Zsn4mLZosWLWT06NFaIcPONqVLl9ZtZAEEn3zyiXz99ddSoUIF3d71+eeflzp16sRstwm7cuXKSc2aNfWij2lg7BnfunVr3dXqgQcekJEjR2p/qDKhn3//+99a7YNPVatWVfDB8d9++61cv35dL/YbNmyQTz/9VF/Dbl0Ao//3//6fTsGimjh9+nT9d7Vq1QRgi4oYdvkCyKGtWrVKAA/4m+e0dGxAxXGoqmHHnL///e/6b5x/8uTJ6jOqdtBlzpw5+gP/sEMXtADIYCzFihVTrbwBqq/6wQ9UKaE/IO+9994TTA+3bdtWd9B58cUXJSoqSsflHFe5cmXVAvu5w+bSpUu6cxgAEGNHhRzw5DTsYY4+/vvf/+qOVNgSt2fPnoJ+KlWqJDVq1JCOHTvKuHHj9Cd16tTSrVs3/VLz+eef65ixfS4q0B9++KH2nyNHDo0/YggARKy8+YE49O3bVx588EGtXvfr18/vd4KnxtgsA/mFuGFJCGYHML4UKVJoviJfUC1v3ry5+jx06FBZtmyZ+vDnn3/qTnDYlah9+/aanxgD8mnjxo0+5/yhQ4fkscceE7y/kPPYghPAC3+Q27lz59a+UTGG5sg9brTgd9hpYLECBFSLg0PXqEBiUACVQuzpPmrUKEFFLnZD1QiVLeypDkgZPHiwggem2l9//XW1AyDhgo8lAoAMwCBADTao2qE6BmgA3CZLlkzWrl0rmN4E+Dz66KMKogDkDh066A/A6OGHH1ZXsNwAQIS95gE38OfJJ5/UvwE6AFIO/E2aNEnatGmjU7OAUoAHQAVgCUhDVQznQQMUzJgxQxo1ahQzZE9ARd8YS9myZRV0AYXwE3vdA0wBf5juzZs3r1YNMdVesWJF3V4XFVgA2qlTpxR4UIXzBqi+6Ldu3bqYKjaAEGAOgJo7d64CFLT88ssvFRShJXR+6623tAoOsFq8eHHM2LG3O74I4AsIljAABp02depUjRsgFeAPAEf/0AdwDujHUpA+ffooQOK82EIYv9GfM8V/8OBBjTO2ggWIISZo0dHRqgNiENsPxObll1/WvELeIC+chi9K6D92g7/IL6d5AiqgD/4C1pEvsP/qq6/0ixLAGg15gil4VDGhEb4cYRkGoN2Zmkeuv/nmmwqVgFeM25eYIeeRlwD3Y8eOqY6IB/IXX8qQm4UKFVJod3SHtsglNirgFgUIqG6JJMdBBcKkACDr3Xff1Qt4/fr17/ECQAYo+e2337Qah+lIwNmvv/6q08sAVFTzcIEFuKEqCKgAMACWsF89ABVTmYA4rOdDhbR69eoKWajooS/YAyoAhPAFgArwRcULQIb+cIEHGKM6+fvvv2vVFDCDShgqUgBEVLkAQ1g7iErhzz//rFAGmIGf2ALx8OHDejzAHBDiNE9ARTUY07r4wfkA1BgPQLxEiRLqE6qmAI1hw4ZJ3bp11WfAGdYqonoHYEcFDRW7+AA1Pv2wrhMVzaVLlyrwAhzRUNH717/+FTPFj3FjjFhDiao3qnaAI2gI7dAPYBnrHbFsA18CAFFOw98BiMgFABuWU+DntddeU9DDuREbaIsvMgDRKVOmaD+3bt2KAVQAJuAe50EFEV9qsATAAdS4/Ihrih/jQcU8dgOEe063O4AKoMcXC8QN1W7ohCp5rVq19MsPYgO/AfSAdHzxwXiRj2io5nbq1CkGIDFOrD/FbAAq1g6gxhcz6JwvXz79ooT3D2YA4gJUxBU+xf6yFKaPA56WCgRNAQJq0KRkR1QgMhVwqkWoaqFSiYapSIArLtQALVzIHeAAIGGqF8fgogpABTju27dPK3W4wAMEAKu4UAMIHUDFGkxMs2OdJ0AOU8G4+GMaHNU7gPAzzzwTA6ieEOUJqABMAAMAEaAKIAH4ACodCMU4sNxg/vz56h+ml511mPArZ86c8QIqpngB4oAMVHGdBlgDhOE3xgk90B/Gj4olIA6g7LSGDRsq5MUHqPHphy8G+fPn1+5QzQPQo2oZG1AxhVy7dm1dL1mvXj3V/dy5c1K4cGGd2nfGDmjFOkv4P3DgwLuSHhqj4QsDIA4xwXjQUO3DFwtURgFqTgMUwx+ngoovKKhc44sJjnVi4ABqXH7EBaiYeoe2sRvAFzF0mgOoyFec18lnQDaWl+DvAHpUaufNm6e/UVlHDAGoqJKjqgqQRMydm7ZQDUWO4v+oxjuAGl/M8EUL1WJnzaqzVhgaYAkEvqzBH2iKajXgmIAamZ+/bh41AdXN0eXYqEAIFLh8+bICGNYy4gKNfwMw0LDmEYAEmMRFG+CDdam40AJ0AKq+Aiqqj7jIA5wARgA7wArOCRjC1CsquJ4VVFTzUIFCcwAVFTJMZaM62r9/f60CAggwtY5KHabB8Vr27Nl17SGmqHHx97xBxxdABbBgSh/jBmCg0oqqKeAGY4APaM7aXYAx7tQGTKLqB/CHr5heBkwFCqiYRscSC8AR/AAEY1r6ypUrOpWPKjaqxKjkAnwA+JiKxpcL/B9fCjzHHh+govLprAVFxdSZDseyAlQa8QUA50JVGHpjjLABaDmACijDkgD4g6qyEz9fABX+YonB448/HpP5gGRMg8duzg1MsQEV48XSAqx7xRcJ+ImqP77QYOofyzHwhQbVc3whQ14CUPE+QP4hftAO60EBzcgz6ACQxZcvXwAVeQMdnKcmOLMO+AKGLw3oBxpBNywDQBzxXvMcdwje+jwFFUhQBQioCSovO6cCkaEA1gXicTi4mKPhAo7pUVR2MCUPOAKIOs1ZDoDXcaOHt2oSYAYXf6eCihuI0ADCgChUAjENj+opjgMYoNqFvn788Uedho4NqA7k4aIOCEVf8BV+AHoBVaiMoaKHhkohYApVRE9Ic6azY0/xx37MFKqBWKPrnAfLDZxHbwFcAI+o4gLE0LCuFXCICiSas84zrkcg+aqfM02OsUI7jA/nwHIHxA0NcXKWazgxxNgB/J5j/+OPP3QpgrcKKoAaX0acm66wfGL58uWybds21RD5geUb+CKAhml0fLnA+lwAKqqmqFbiiwiAHsAJqEd+QVvAtVNBje0H+gUIOxVOJ9cQc+RF7IZ1oVjP7DRPjbG8AWNA3NAwhY7KMqqXAGDoBCAERKPiCp3wRcIzx2GH/Ae8OhVxAOX9YoZ8x5cAZ6kF+sHyFcQB/uCcOAaVZ1SHAbz40gPt2KiAmxQgoLopmhwLFQizAoAGVOdQRcUdz54N6zNxgcXaTUz1+tNQicL6VYANLsRY6+jcsQywwgUd0OTPXcyww1Q/INQTVOAXKqS44QVVTkCwScP6SpwHYwBs+dJw/EMPPRTUZ1siNpi6ju0DKql4OoLzIHjECaCO9Z+++uvLmDyPQf/Q3NsD5jEljqcJoAKNZRKoZiJvAGX3a4BRLAEJxgP/ETfcSe/clIRzQ0NU25GLuCkKlVJM6WOdKfI6ruYsBUCeei4ruN94PP+OfHWeh+rkJMaL86JK78xa+NMnj6UCNitAQLU5OvSNClABVcABVKy3Y3O3AqhOoxqOLzpOi/3EgHApAPjF2lCnwg4/PB+vFpdf+LKDyicqxpiuD1YDHOOJD6iw4ssHGxVwkwIEVDdFk2OhAi5VAHezoyrm3ITj0mFyWP9TAFVdLEE4c+aMgh3uaLelobKKdarYhAJVZtxA5kuVHeCNdbTOI86CMR7ceIhzx1e9DcZ52AcVCIcCBNRwqM5zUgEqQAWoABWgAlSACsSpAAGVyUEFqAAVoAJUgApQASpglQIEVKvCQWeoABWgAlSAClABKkAF/j+U+m4sv5dNfAAAAABJRU5ErkJggg==",
"text/plain": [
"<VegaLite 4 object>\n",
"\n",
"If you see this message, it means the renderer has not been properly enabled\n",
"for the frontend that you are using. For more information, see\n",
"https://altair-viz.github.io/user_guide/troubleshooting.html\n"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"blocking_rule = \"l.dob = r.dob\"\n",
"training_session_dob = linker.estimate_parameters_using_expectation_maximisation(blocking_rule)\n",
"training_session_dob.match_weights_interactive_history_chart()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The final match weights can be viewed in the match weights chart:"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"data": {
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{
"bayes_factor": 2.5796568427437232e-05,
"bayes_factor_description": "The probability that two random records drawn at random match is 0.000 or one in 38,765.8 records.This is equivalent to a starting match weight of -15.242.",
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{
"bayes_factor": 42.57918965897765,
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{
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"bayes_factor": 0.22072389682447663,
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"bayes_factor": 996.9629081638653,
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"u_probability_description": "Amongst non-matching record comparisons, 0.08% of records are in the exact match comparison level"
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"bayes_factor": 391.4377585053876,
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},
"selection": {
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},
"title": {
"subtitle": "Use mousewheel to zoom",
"text": "Model parameters (components of final match weight)"
},
"vconcat": [
{
"encoding": {
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10
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"tooltip": [
{
"field": "comparison_name",
"title": "Comparison name",
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{
"field": "probability_two_random_records_match",
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"title": "Probability two random records match",
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},
{
"field": "log2_bayes_factor",
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"title": "Equivalent match weight",
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},
{
"field": "bayes_factor_description",
"title": "Match weight description",
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}
],
"x": {
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"labels": false,
"ticks": false,
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"field": "log2_bayes_factor",
"scale": {
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-10,
10
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},
"type": "quantitative"
},
"y": {
"axis": {
"title": "Prior (starting) match weight",
"titleAlign": "right",
"titleAngle": 0,
"titleFontWeight": "normal"
},
"field": "label_for_charts",
"sort": {
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},
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"mark": {
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},
"transform": [
{
"filter": "(datum.comparison_name == 'probability_two_random_records_match')"
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},
{
"encoding": {
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10
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},
"title": "Match weight",
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"row": {
"field": "comparison_name",
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"field": "comparison_sort_order"
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"tooltip": [
{
"field": "comparison_name",
"title": "Comparison name",
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{
"field": "label_for_charts",
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"type": "ordinal"
},
{
"field": "sql_condition",
"title": "SQL condition",
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},
{
"field": "m_probability",
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"title": "M probability",
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},
{
"field": "u_probability",
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"title": "U probability",
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},
{
"field": "bayes_factor",
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"title": "Bayes factor = m/u",
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{
"field": "log2_bayes_factor",
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"title": "Match weight = log2(m/u)",
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},
{
"field": "bayes_factor_description",
"title": "Match weight description",
"type": "nominal"
}
],
"x": {
"axis": {
"title": "Comparison level match weight = log2(m/u)"
},
"field": "log2_bayes_factor",
"scale": {
"domain": [
-10,
10
]
},
"type": "quantitative"
},
"y": {
"axis": {
"title": null
},
"field": "label_for_charts",
"sort": {
"field": "comparison_vector_value",
"order": "descending"
},
"type": "nominal"
}
},
"mark": {
"clip": true,
"type": "bar"
},
"resolve": {
"axis": {
"y": "independent"
},
"scale": {
"y": "independent"
}
},
"selection": {
"zoom_selector": {
"bind": "scales",
"encodings": [
"x"
],
"type": "interval"
}
},
"transform": [
{
"filter": "(datum.comparison_name != 'probability_two_random_records_match')"
}
]
}
]
},
"image/png": 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",
"text/plain": [
"<VegaLite 4 object>\n",
"\n",
"If you see this message, it means the renderer has not been properly enabled\n",
"for the frontend that you are using. For more information, see\n",
"https://altair-viz.github.io/user_guide/troubleshooting.html\n"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"linker.match_weights_chart()"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>match_weight</th>\n",
" <th>match_probability</th>\n",
" <th>unique_id_l</th>\n",
" <th>unique_id_r</th>\n",
" <th>first_name_l</th>\n",
" <th>first_name_r</th>\n",
" <th>gamma_first_name</th>\n",
" <th>bf_first_name</th>\n",
" <th>surname_l</th>\n",
" <th>surname_r</th>\n",
" <th>...</th>\n",
" <th>bf_postcode_fake</th>\n",
" <th>birth_place_l</th>\n",
" <th>birth_place_r</th>\n",
" <th>gamma_birth_place</th>\n",
" <th>bf_birth_place</th>\n",
" <th>occupation_l</th>\n",
" <th>occupation_r</th>\n",
" <th>gamma_occupation</th>\n",
" <th>bf_occupation</th>\n",
" <th>match_key</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>16.545601</td>\n",
" <td>0.999990</td>\n",
" <td>Q2296770-1</td>\n",
" <td>Q2296770-14</td>\n",
" <td>thomas</td>\n",
" <td>thomas</td>\n",
" <td>3</td>\n",
" <td>42.57919</td>\n",
" <td>chudleigh</td>\n",
" <td>chudleigh</td>\n",
" <td>...</td>\n",
" <td>231.365812</td>\n",
" <td>devon</td>\n",
" <td>NaN</td>\n",
" <td>-1</td>\n",
" <td>1.000000</td>\n",
" <td>politician</td>\n",
" <td>politician</td>\n",
" <td>1</td>\n",
" <td>25.912637</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2.292304</td>\n",
" <td>0.830462</td>\n",
" <td>Q2296770-10</td>\n",
" <td>Q2296770-14</td>\n",
" <td>thomas</td>\n",
" <td>thomas</td>\n",
" <td>3</td>\n",
" <td>42.57919</td>\n",
" <td>chudleigh</td>\n",
" <td>chudleigh</td>\n",
" <td>...</td>\n",
" <td>0.172624</td>\n",
" <td>devon</td>\n",
" <td>NaN</td>\n",
" <td>-1</td>\n",
" <td>1.000000</td>\n",
" <td>politician</td>\n",
" <td>politician</td>\n",
" <td>1</td>\n",
" <td>25.912637</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>22.370232</td>\n",
" <td>1.000000</td>\n",
" <td>Q1443188-1</td>\n",
" <td>Q1443188-3</td>\n",
" <td>frank</td>\n",
" <td>frank</td>\n",
" <td>3</td>\n",
" <td>42.57919</td>\n",
" <td>brightman</td>\n",
" <td>brightman</td>\n",
" <td>...</td>\n",
" <td>4435.362998</td>\n",
" <td>bristol</td>\n",
" <td>bristol, city of</td>\n",
" <td>0</td>\n",
" <td>0.162352</td>\n",
" <td>liturgist</td>\n",
" <td>liturgist</td>\n",
" <td>1</td>\n",
" <td>25.912637</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>22.370232</td>\n",
" <td>1.000000</td>\n",
" <td>Q1443188-2</td>\n",
" <td>Q1443188-3</td>\n",
" <td>frank</td>\n",
" <td>frank</td>\n",
" <td>3</td>\n",
" <td>42.57919</td>\n",
" <td>brightman</td>\n",
" <td>brightman</td>\n",
" <td>...</td>\n",
" <td>4435.362998</td>\n",
" <td>bristol</td>\n",
" <td>bristol, city of</td>\n",
" <td>0</td>\n",
" <td>0.162352</td>\n",
" <td>liturgist</td>\n",
" <td>liturgist</td>\n",
" <td>1</td>\n",
" <td>25.912637</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>6.157277</td>\n",
" <td>0.986182</td>\n",
" <td>Q1443188-4</td>\n",
" <td>Q1443188-5</td>\n",
" <td>francis</td>\n",
" <td>francis</td>\n",
" <td>3</td>\n",
" <td>42.57919</td>\n",
" <td>brightman</td>\n",
" <td>brightman</td>\n",
" <td>...</td>\n",
" <td>0.172624</td>\n",
" <td>NaN</td>\n",
" <td>bristol, city of</td>\n",
" <td>-1</td>\n",
" <td>1.000000</td>\n",
" <td>liturgist</td>\n",
" <td>liturgist</td>\n",
" <td>1</td>\n",
" <td>25.912637</td>\n",
" <td>0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows × 29 columns</p>\n",
"</div>"
],
"text/plain": [
" match_weight match_probability unique_id_l unique_id_r first_name_l \\\n",
"0 16.545601 0.999990 Q2296770-1 Q2296770-14 thomas \n",
"1 2.292304 0.830462 Q2296770-10 Q2296770-14 thomas \n",
"2 22.370232 1.000000 Q1443188-1 Q1443188-3 frank \n",
"3 22.370232 1.000000 Q1443188-2 Q1443188-3 frank \n",
"4 6.157277 0.986182 Q1443188-4 Q1443188-5 francis \n",
"\n",
" first_name_r gamma_first_name bf_first_name surname_l surname_r ... \\\n",
"0 thomas 3 42.57919 chudleigh chudleigh ... \n",
"1 thomas 3 42.57919 chudleigh chudleigh ... \n",
"2 frank 3 42.57919 brightman brightman ... \n",
"3 frank 3 42.57919 brightman brightman ... \n",
"4 francis 3 42.57919 brightman brightman ... \n",
"\n",
" bf_postcode_fake birth_place_l birth_place_r gamma_birth_place \\\n",
"0 231.365812 devon NaN -1 \n",
"1 0.172624 devon NaN -1 \n",
"2 4435.362998 bristol bristol, city of 0 \n",
"3 4435.362998 bristol bristol, city of 0 \n",
"4 0.172624 NaN bristol, city of -1 \n",
"\n",
" bf_birth_place occupation_l occupation_r gamma_occupation bf_occupation \\\n",
"0 1.000000 politician politician 1 25.912637 \n",
"1 1.000000 politician politician 1 25.912637 \n",
"2 0.162352 liturgist liturgist 1 25.912637 \n",
"3 0.162352 liturgist liturgist 1 25.912637 \n",
"4 1.000000 liturgist liturgist 1 25.912637 \n",
"\n",
" match_key \n",
"0 0 \n",
"1 0 \n",
"2 0 \n",
"3 0 \n",
"4 0 \n",
"\n",
"[5 rows x 29 columns]"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_predict = linker.predict()\n",
"df_e = df_predict.as_pandas_dataframe(limit=5)\n",
"df_e"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"You can also view rows in this dataset as a waterfall chart as follows:"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.vegalite.v4+json": {
"$schema": "https://vega.github.io/schema/vega-lite/v5.2.0.json",
"config": {
"view": {
"continuousHeight": 300,
"continuousWidth": 400
}
},
"data": {
"values": [
{
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"label_for_charts": "Starting match weight (prior)",
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"value_l": "",
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{
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"bayes_factor_description": "If comparison level is `exact match` then comparison is 42.58 times more likely to be a match",
"column_name": "first_name",
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{
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"bayes_factor_description": "If comparison level is `exact match` then comparison is 996.96 times more likely to be a match",
"column_name": "surname",
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{
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"bayes_factor_description": "If comparison level is `levenshtein <= 1` then comparison is 14.57 times more likely to be a match",
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"comparison_vector_value": 2,
"label_for_charts": "levenshtein <= 1",
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{
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"bayes_factor_description": "If comparison level is `levenshtein <= 2` then comparison is 231.37 times more likely to be a match",
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{
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"bayes_factor_description": "If comparison level is `null` then comparison is 1.00 times more likely to be a match",
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{
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"bayes_factor": 25.91263688262942,
"bayes_factor_description": "If comparison level is `exact match` then comparison is 25.91 times more likely to be a match",
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"log2_bayes_factor": 4.6955839272300235,
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"u_probability": 0.03480827496026209,
"value_l": "politician",
"value_r": "politician"
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{
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"column_name": "Final score",
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"label_for_charts": "Final score",
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{
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"label_for_charts": "Starting match weight (prior)",
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"m_probability": null,
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},
{
"bar_sort_order": 1,
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"bayes_factor_description": "If comparison level is `exact match` then comparison is 42.58 times more likely to be a match",
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"label_for_charts": "Exact match",
"log2_bayes_factor": 5.412076588451669,
"m_probability": 0.5558653370649939,
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{
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"bayes_factor": 996.9629081638653,
"bayes_factor_description": "If comparison level is `exact match` then comparison is 996.96 times more likely to be a match",
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"log2_bayes_factor": 9.961396020172025,
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"u_probability": 0.000790451099853488,
"value_l": "chudleigh",
"value_r": "chudleigh"
},
{
"bar_sort_order": 3,
"bayes_factor": 1,
"bayes_factor_description": "If comparison level is `null` then comparison is 1.00 times more likely to be a match",
"column_name": "dob",
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"label_for_charts": "Null",
"log2_bayes_factor": 0,
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"record_number": 1,
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{
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"bayes_factor": 0.17262441259524716,
"bayes_factor_description": "If comparison level is `all other comparisons` then comparison is 5.79 times less likely to be a match",
"column_name": "postcode_fake",
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"label_for_charts": "All other comparisons",
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{
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"bayes_factor": 1,
"bayes_factor_description": "If comparison level is `null` then comparison is 1.00 times more likely to be a match",
"column_name": "birth_place",
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"label_for_charts": "Null",
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},
{
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"bayes_factor_description": "If comparison level is `exact match` then comparison is 25.91 times more likely to be a match",
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"value_l": "politician",
"value_r": "politician"
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{
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{
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{
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"value_l": "frank",
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{
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"bayes_factor_description": "If comparison level is `exact match` then comparison is 996.96 times more likely to be a match",
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"value_l": "brightman",
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{
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"bayes_factor_description": "If comparison level is `exact match` then comparison is 265.32 times more likely to be a match",
"column_name": "dob",
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"value_r": "1856-06-18"
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{
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"bayes_factor": 4435.3629982340935,
"bayes_factor_description": "If comparison level is `exact match` then comparison is 4,435.36 times more likely to be a match",
"column_name": "postcode_fake",
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"label_for_charts": "Exact match",
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"sql_condition": "postcode_fake_l = postcode_fake_r",
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"u_probability": 0.00015456118629170805,
"value_l": "bs2 0el",
"value_r": "bs2 0el"
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{
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"bayes_factor": 0.16235239901603787,
"bayes_factor_description": "If comparison level is `all other comparisons` then comparison is 6.16 times less likely to be a match",
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"label_for_charts": "All other comparisons",
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{
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"bayes_factor_description": "If comparison level is `exact match` then comparison is 25.91 times more likely to be a match",
"column_name": "occupation",
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"label_for_charts": "Exact match",
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"sql_condition": "occupation_l = occupation_r",
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"value_l": "liturgist",
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{
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"column_name": "Final score",
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"label_for_charts": "Final score",
"log2_bayes_factor": 22.370232209652713,
"m_probability": null,
"record_number": 2,
"sql_condition": null,
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"u_probability": null,
"value_l": "",
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},
{
"bar_sort_order": 0,
"bayes_factor": 2.5796568427437232e-05,
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"column_name": "Prior",
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"label_for_charts": "Starting match weight (prior)",
"log2_bayes_factor": -15.242461309640488,
"m_probability": null,
"record_number": 3,
"sql_condition": null,
"term_frequency_adjustment": null,
"u_probability": null,
"value_l": "",
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},
{
"bar_sort_order": 1,
"bayes_factor": 42.57918965897765,
"bayes_factor_description": "If comparison level is `exact match` then comparison is 42.58 times more likely to be a match",
"column_name": "first_name",
"comparison_vector_value": 3,
"label_for_charts": "Exact match",
"log2_bayes_factor": 5.412076588451669,
"m_probability": 0.5558653370649939,
"record_number": 3,
"sql_condition": "first_name_l = first_name_r",
"term_frequency_adjustment": false,
"u_probability": 0.01305485946343725,
"value_l": "frank",
"value_r": "frank"
},
{
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"bayes_factor": 996.9629081638653,
"bayes_factor_description": "If comparison level is `exact match` then comparison is 996.96 times more likely to be a match",
"column_name": "surname",
"comparison_vector_value": 3,
"label_for_charts": "Exact match",
"log2_bayes_factor": 9.961396020172025,
"m_probability": 0.7880504272712593,
"record_number": 3,
"sql_condition": "surname_l = surname_r",
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"u_probability": 0.000790451099853488,
"value_l": "brightman",
"value_r": "brightman"
},
{
"bar_sort_order": 3,
"bayes_factor": 265.32189199903297,
"bayes_factor_description": "If comparison level is `exact match` then comparison is 265.32 times more likely to be a match",
"column_name": "dob",
"comparison_vector_value": 3,
"label_for_charts": "Exact match",
"log2_bayes_factor": 8.051599908595803,
"m_probability": 0.6103033862603122,
"record_number": 3,
"sql_condition": "dob_l = dob_r",
"term_frequency_adjustment": false,
"u_probability": 0.00230023757806814,
"value_l": "1856-06-18",
"value_r": "1856-06-18"
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",
"text/plain": [
"<VegaLite 4 object>\n",
"\n",
"If you see this message, it means the renderer has not been properly enabled\n",
"for the frontend that you are using. For more information, see\n",
"https://altair-viz.github.io/user_guide/troubleshooting.html\n"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from splink.charts import waterfall_chart\n",
"records_to_plot = df_e.to_dict(orient=\"records\")\n",
"linker.waterfall_chart(records_to_plot, filter_nulls=False)"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Completed iteration 1, root rows count 669\n",
"Completed iteration 2, root rows count 147\n",
"Completed iteration 3, root rows count 43\n",
"Completed iteration 4, root rows count 11\n",
"Completed iteration 5, root rows count 1\n",
"Completed iteration 6, root rows count 0\n"
]
}
],
"source": [
"clusters = linker.cluster_pairwise_predictions_at_threshold(df_predict, threshold_match_probability=0.95)"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [],
"source": [
"linker.cluster_studio_dashboard(df_predict, clusters, \"50k_cluster.html\", sampling_method='by_cluster_size', overwrite=True)"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
" <iframe\n",
" width=\"100%\"\n",
" height=\"1200\"\n",
" src=\"./50k_cluster.html\"\n",
" frameborder=\"0\"\n",
" allowfullscreen\n",
" \n",
" ></iframe>\n",
" "
],
"text/plain": [
"<IPython.lib.display.IFrame at 0x7fb6c2063430>"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from IPython.display import IFrame\n",
"\n",
"IFrame(\n",
" src=\"./50k_cluster.html\", width=\"100%\", height=1200\n",
") "
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.8.3"
},
"vscode": {
"interpreter": {
"hash": "cc173ace240fa2ad02472fa75d75ddd885c067b637b7c50cecea7593995ac3de"
}
}
},
"nbformat": 4,
"nbformat_minor": 4
}