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  "seconds_per_simulation": {
    "t": 0.655694521268364,
    "candidate": 0.6392895533102565
  },
  "notes": [
    "Locally measured paired total_edge differences, CANDIDATE minus T; positive values favor CANDIDATE.",
    "Simulator source: https://github.com/danrobinson/prediction-market-challenge at commit b244530ee2a6247b5f930830eeca9dd950da0e7e; extracted files byte-checked against the pinned source archive before and after execution.",
    "T source: https://api.imdnotdead.fun/artifacts/79af781a0f1882f2a291e045f803714b52128f9938480d54fb43360417b381ac (code string, UTF-8). Both required SHA-256 hashes verified. CANDIDATE differs only in the specified ON line.",
    "Python 3.13.5; one serial in-process loop, no simulator sandbox or workers. For each seed, runner.sample_config(ChallengeConfig(), ParameterVariance(), seed=s); then T and CANDIDATE with the same cfg and engine seed=s. loader.load_strategy_factory is called anew before each engine run; the engine constructs a fresh strategy.",
    "Every seed and every spread is actually simulated for both strategies. Spread is cfg.competitor.spread_ticks. No tuning, config overrides, or synthetic zero differences.",
    "Statistics and ranking use unrounded floating-point differences. per_seed, worst and best display differences to six decimals. sum_d2 is the sum of squared differences; se is sample standard deviation (ddof=1) divided by sqrt(n). Ties in extrema are ordered by increasing seed.",
    "seconds_per_simulation contains mean wall-clock seconds for SimulationEngine construction plus run(), separately for T and CANDIDATE, excluding config sampling, strategy loading, and reporting.",
    "Sources establish the strategy and simulator implementation; all numeric outcome statistics and timings are measurements from this local run, not published results."
  ]
}