1{2 "verdict": "accept",3 "issues": [4 {5 "id": "R1",6 "severity": "minor",7 "location": "out/report.md (Limitations, first bullet); out/claims.json C3 caveat",8 "issue": "The report says the gap of -0.0104 is 'what one expects' from omitted O(1/n^2) terms. At n=30, 1/n^2 is about 0.0011, so the gap would need a second-order coefficient of about 9. That may be plausible at rho=0.8, but no result shows it. One design point cannot show the order of the remainder, as the report's own next bullet says.",9 "suggested_fix": "Change the wording to 'consistent with higher-order terms not captured by the first-order approximation'. Drop 'what one expects' unless a higher-order formula or a sweep over n backs it up."10 },11 {12 "id": "R2",13 "severity": "minor",14 "location": "out/report.md (Data and exclusions)",15 "issue": "The report says the SHA-256 of design.csv matches bundle.json. analysis.py does not compute this hash and no result key records it, so the deliverables cannot check the statement.",16 "suggested_fix": "Compute and record the hash in results.json or a log, or remove the sentence."17 }18 ],19 "summary": "A scratch re-run of analysis.py, using only data/design.csv, reproduced results.json exactly. The code follows the design: n=30, rho=0.8, sigma=1, c=0, a stationary y_1, a fresh series in each of the 20000 replications, and OLS of y_t on (1, y_{t-1}) for t=2..n, cross-checked against lstsq. It uses default_rng with the given seed and no forbidden inputs. All required keys are present and internally consistent: bias_mc = rho_hat_mean - rho, se = sd/sqrt(R), the discrepancy is -9.85 SE, and the baseline se_asymptotic is reported. The claims match the numbers, and the report correctly calls the formula a first-order asymptotic approximation with clear scope limits. Only minor wording and provenance points remain."20}21