[
  {
    "local_id": "C1",
    "type": "empirical",
    "core": true,
    "statement": "In finite Wright-Fisher populations, exact absorbing Markov chain solutions establish that Kimura's continuous diffusion approximation systematically overestimates single-mutant fixation probability, with relative error scaling from 0.29% at weak selection and population size 100 to 3.31% at strong selection and population size 20.",
    "evidence": [
      {
        "result": "R1.err_s01_n100",
        "produced_by": "code/run.py",
        "tolerance": 0.01
      },
      {
        "result": "R1.err_s10_n20",
        "produced_by": "code/run.py",
        "tolerance": 0.01
      }
    ],
    "depends_on": [],
    "confidence": 1.0
  },
  {
    "local_id": "C2",
    "type": "empirical",
    "core": true,
    "statement": "Conditional on fixation, Doob h-transformed discrete absorption times prove near-exact trajectory time symmetry between deleterious and beneficial alleles, with mean conditional fixation times differing by at most 3.02% across all evaluated populations and selection coefficients up to 0.05.",
    "evidence": [
      {
        "result": "R2.max_tau_diff_pct",
        "produced_by": "code/run.py",
        "tolerance": 0.01
      }
    ],
    "depends_on": [],
    "confidence": 1.0
  },
  {
    "local_id": "C3",
    "type": "resource",
    "core": true,
    "statement": "In the neutral limit, discrete Wright-Fisher fixation probability matches the theoretical inverse population size with an absolute residual of 0.0, and conditional fixation time for population size 100 reaches 196.74 generations, converging within 1.63% of the asymptotic bound of 200.0 generations.",
    "evidence": [
      {
        "result": "R3.neutral_res_max",
        "produced_by": "code/run.py",
        "tolerance": 0.000001
      },
      {
        "result": "R3.tau_neutral_n100",
        "produced_by": "code/run.py",
        "tolerance": 0.01
      },
      {
        "result": "R3.max_diff_kimura_ohta_pct",
        "produced_by": "code/run.py",
        "tolerance": 0.01
      }
    ],
    "depends_on": [],
    "confidence": 1.0
  }
]