{
  "ai.module": "evolutionary_biology.rtt3",
  "ai.version": "1.0",
  "ai.purpose": "RTT/3 engine layer for Evolutionary Biology: triadic-substrate integration, multi-regime simulation hooks, lineage-aware reasoning, and hybrid-canon scaffolding.",
  "ai.keywords": [
    "evolution",
    "adaptive resonance",
    "lineage",
    "substrate",
    "multi-regime",
    "simulation",
    "rtt3"
  ],

  "engine": {
    "layer": "RTT/3",
    "description": "Integrates Evolutionary Biology into the full triadic substrate, enabling multi-regime reasoning, lineage tracking, and hybrid-canon simulation."
  },

  "substrate_integration": {
    "mapping": {
      "structure": "Lineage maps to substrate memory channels that preserve adaptive information across generations.",
      "variation": "Novelty arises from perturbations in substrate-level encoding and developmental pathways.",
      "selection": "Environmental resonance patterns filter lineage trajectories based on fit.",
      "adaptation": "Stable resonance alignment between lineage and environment emerges over repeated cycles."
    },
    "constraints": [
      "Lineage continuity is required for adaptive resonance.",
      "Environmental stability shapes resonance basins.",
      "Substrate-level invariants limit mutation and variation pathways."
    ]
  },

  "multi_regime": {
    "R1": {
      "behavior": "Primitive replicator dynamics; minimal lineage memory; high stochasticity.",
      "notes": "Adaptive resonance weak; dominated by noise and drift."
    },
    "R2": {
      "behavior": "Genetic and molecular mechanisms partially stabilize adaptive patterns.",
      "notes": "Lineage memory strengthens; variation becomes structured."
    },
    "R3": {
      "behavior": "Full population-level adaptive resonance; stable selection and lineage propagation.",
      "notes": "Evolution expresses coherent, multi-generational adaptive cycles."
    }
  },

  "lineage": {
    "tracking": [
      "genetic_memory",
      "epigenetic_modulation",
      "developmental_constraints",
      "multi-generational_resonance"
    ],
    "effects": [
      "stabilizes adaptive traits",
      "reduces apparent randomness",
      "creates long-term evolutionary trajectories",
      "enables cross-regime coherence"
    ]
  },

  "simulation": {
    "hooks": [
      {
        "name": "adaptive_resonance_sim",
        "description": "Simulates variation → selection → inheritance cycles as substrate-driven resonance loops."
      },
      {
        "name": "lineage_memory_sim",
        "description": "Models how genetic and epigenetic memory stabilize adaptive patterns."
      },
      {
        "name": "environmental_resonance_sim",
        "description": "Simulates how environmental constraints shape selection gradients and adaptive basins."
      }
    ],
    "notes": "Simulations operate on resonance patterns and lineage structures, not on metaphysical or teleological assumptions."
  },

  "hybrid_canon": {
    "interfaces": [
      "genetics.rtt3",
      "ecology.rtt3",
      "information_theory.rtt3",
      "chaos_theory.rtt3"
    ],
    "purpose": "Enable agentic AIs to reason across biological, ecological, and informational modules using shared substrate invariants.",
    "scaffolding": [
      "triadic substrate mapping",
      "multi-regime coherence",
      "lineage-aware propagation",
      "adaptive resonance integration"
    ]
  },

  "coherence": {
    "invariants": [
      "lineage continuity",
      "structured variation",
      "environmental resonance alignment",
      "multi-generational stability"
    ],
    "failure_modes": [
      "lineage collapse",
      "environmental discontinuity",
      "runaway drift",
      "loss of adaptive resonance"
    ]
  }
}