code-reviewer
FreeDetect whether approved modeling code is Python or MATLAB/Beita Tianyuan and route it to the matching reviewer using the compact named-check review contract.
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Faithfully append a human modeler's choice and rationale to one canonical per-subquestion JSONL decision ledger. Use after a choice card is answered or when migrating legacy decision artifacts; never originate or improve the decision.
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Make human judgment traceable without multiplying decision files.
methods/Qx/qx_decisions.jsonl
One JSON object per line. Records are append-only.
Use planning/framing_decisions.jsonl for global/pre-Qx framing decisions.
{
"schema_version": 1,
"decision_id": "q1_method_choice",
"decision_type": "method_choice",
"status": "DECIDED",
"decided_by": "human",
"captured_in_mode": "learning",
"choice": "M2",
"rationale": "Human-authored reason tied to evidence.",
"evidence_refs": ["methods/Q1/probes/risk_probe_summary.json"],
"decided_at": "ISO-8601",
"supersedes": null
}
Optional structured fields may include confidence, rejected alternatives, round action, claim scope, assumption labels, or fallback activation.
supersedes; never overwrite history.qx_method_card.md only when the decision changes method state.Typical values:
framingmethod_choicefallback_activationresult_verdictstability_verdictassumption_necessityclaim_scopepackage_signoffsubmission_authorizationMark a decision stale only when its cited evidence materially changed:
decision_stale record naming the old decision and changed evidence;Read legacy:
methods/Qx/qx_decision_log.mdmethods/Qx/decisions/*_modeler_decision.mdMigrate only completed human decisions. Preserve original timestamps and source paths when available. Do not convert PENDING placeholders into decisions.
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Detect whether approved modeling code is Python or MATLAB/Beita Tianyuan and route it to the matching reviewer using the compact named-check review contract.
The English summary is being prepared. Showing the original description.
Audit whether the semantic evidence required by the active lean or submission profile exists and is current, without requiring one verbose artifact per skill or an arbitrary number of pass bullets.
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Run scoped or final cross-media consistency checks for mathematical-modeling artifacts, comparing canonical numbers, symbols, parameters, decisions, files, and paper claims without performing full-workspace audits for low-risk changes.
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Map contest attachments to subquestions, audit and clean raw data, and emit one reusable data profile with quality, coverage, imbalance, concentration, and method-readiness evidence for downstream risk screening.
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Build one compact choice card at a genuine mathematical-modeling judgment point. Use before method screening, after a meaningful experiment, or before final claim/freeze approval so the human chooses the trade-off while AI handles mechanical consequences.
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Plan the smallest set of diagnostic, comparison, paper, and appendix figures or tables needed to support verified mathematical-modeling decisions and claims.
The English summary is being prepared. Showing the original description.