{
  "role": "code_methods_reviewer",
  "decision": "pass",
  "summary": "The implementation matches the locked registration: the weather-augmented challenger is compared against the active champion on identical held-out player-week denominators (20,700 rows, reconciling exactly across both the position and season breakdowns), uses only the four registered weather features, evaluates six rolling future-season windows (2019-2024) with prior-seasons-only training and pre-cutoff forecast availability, reports game-cluster bootstrap uncertainty, and passes all locked gates with a code-owned promotion decision. Numeric claims in the article, tables, claim ledger, and deterministic comparison facts are internally consistent (overall +0.568% improvement, CI excluding zero, 6/6 winning windows, max position regression +0.239% at RB). Remaining issues are bounded warnings, not blockers.",
  "findings": [
    {
      "severity": "warning",
      "check": "freshness",
      "evidence": "freshness_requirement.expected_season is 2025 with mode 'latest_completed_season', but analysis.data_through and all evaluation windows end at 2024. minimum_season (2024) is met and the locked registration specifies exactly the 2019-2024 windows, so the design matches the registration; however, the expected-season value is not satisfied and no exception is recorded.",
      "recommendation": "Record an explicit freshness exception or reconcile the expected_season value with the locked window set."
    },
    {
      "severity": "warning",
      "check": "missingness_and_attrition",
      "evidence": "missingness reports input_rows 20700, excluded_rows 0, and an empty missing_by_field map, while the methods and locked sensitivity state 'weather-missing games removed from both models.' The evidence cannot distinguish whether no games were weather-missing or whether attrition occurred upstream before the reported input count, so the attrition handling cannot be fully verified from the bundle.",
      "recommendation": "Report the pre-removal game/row count and the number of weather-missing games removed, so the identical-denominator claim is auditable end to end."
    },
    {
      "severity": "warning",
      "check": "reproducibility",
      "evidence": "The bundle asserts a 'deterministic rerun' with matching prediction hashes and a '4,000-draw deterministic cluster bootstrap,' and artifact hashes are listed for generated/analysis.py and the model artifacts, but no explicit random seed value or bootstrap seed is recorded anywhere in the evidence, so deterministic replay cannot be independently verified from the bundle alone.",
      "recommendation": "Record the bootstrap and training seeds (or the deterministic hash chain inputs) in the scorecard or analysis metadata."
    },
    {
      "severity": "warning",
      "check": "evidence_completeness",
      "evidence": "Only artifact hashes are supplied for the registration, scorecard, decision, and analysis code; the contents of experiment-registration.json, champion-decision.json, and generated/analysis.py are not present in the bundle, so gate logic and registration fidelity are verified indirectly through the analysis and claim ledger rather than by direct inspection.",
      "recommendation": "Include the registration and decision artifacts (or their verified extracts) in future review bundles so gate logic can be inspected directly."
    }
  ],
  "artifacts_reviewed": [
    "analysis.json",
    "article-draft.json",
    "article.md",
    "claim-ledger.json",
    "research-registration.json (hash only)",
    "data/model/champion-decision.json (hash only)",
    "data/model/scorecard.json (hash only)",
    "generated/analysis.py (hash only)",
    "provenance.json",
    "publication-assets.json",
    "search-demand-brief.json",
    "figures/weekly-weather-projection-error.png (hash only)"
  ],
  "model": "z-ai/glm-5.3",
  "created_at": "2026-09-03T17:23:52.049740+00:00"
}