1{2 "verdict": "accept",3 "issues": [4 {5 "id": "R1",6 "severity": "minor",7 "location": "out/report.md (Limitations, first bullet)",8 "issue": "The report says the Monte Carlo 'shows that, at this point, the first-order formula misses an O(n^-2) remainder'. A single design point can show a gap of about 0.010 (9.9 MC SE), but it cannot show the order of that gap. Calling it O(n^-2) comes from asymptotic theory, not from this evidence. C3's caveat already admits this, so the report is less careful than its own claims file.",9 "suggested_fix": "Reword to: 'the first-order formula misses about 0.010 (8%) of the bias at this point; theory attributes this to higher-order terms, which a single design point cannot verify.'"10 },11 {12 "id": "R2",13 "severity": "minor",14 "location": "out/report.md (Uncertainty)",15 "issue": "The statement that a different seed 'would move bias_mc by about ±0.002' is a heuristic (about 2 MC SE, roughly a 95% range), not a computed result. It is presented loosely, as if it were a bound.",16 "suggested_fix": "Say that a different seed would typically move bias_mc by within about ±2·bias_mc_se ≈ ±0.002, with about 95% probability."17 }18 ],19 "summary": "In a scratch copy, analysis.py reproduces results.json exactly and deterministically. It reads only data/design.csv and follows the design: c=0, a stationary y_1 ~ N(0, σ²/(1-ρ²)), a fresh series in every replication, OLS with an intercept on t=2..n (checked against lstsq), R=20000, and the specified seed. All required keys are present and consistent. bias_analytical=-(1+3·0.8)/30=-0.1133, bias_mc=-0.1237, SE=0.00105, discrepancy=-9.85 SE, and se_asymptotic=0.1095 are correct. The counts are also consistent: 7 input rows, 0 replications excluded, 20000 analysed. The report describes the formula correctly as a leading-order asymptotic approximation with no finite-sample error bound, gives the n-1 sensitivity variant, and limits the conclusions to this one design point. The claims match the numbers. Only minor wording issues remain."20}21