1{"verdict":"accept",2"issues":[3{"id":"R1","severity":"minor","location":"out/claims.json C3","issue":"C3's -6.1 SE figure is not stored in any results key; it is described as 'computed in the P4 checks'. It does check out: (-0.12371+0.11724)/0.001053 = -6.14.","suggested_fix":"Add a supp_discrepancy_se_units_n_minus_1 key to results.json so the claim can be traced to a results key."},4{"id":"R2","severity":"minor","location":"out/report.md Uncertainty","issue":"The statement that a different seed would move bias_mc by 'roughly ±1 bias_mc_se' is loose: ±1 SE is about a 68% range, and differences of about 2 SE are common. In an independent re-simulation with a different draw order, I got -0.1219, about 1.7 SE from the reported value. That is consistent with Monte Carlo noise and does not change any conclusion.","suggested_fix":"Change the wording to say that a different seed would typically move bias_mc by about 1-2 bias_mc_se."}5],6"summary":"All required keys are present. Re-running analysis.py in a scratch copy reproduced results.json exactly. The design is followed: n=30, rho=0.8, sigma=1, c=0, stationary y_1, intercept, R=20000, seed 20261029. The OLS slope is computed correctly. Checks against a fully explicit lstsq loop give a consistent bias of about -0.122 vs -0.124, which is within Monte Carlo error. The derived quantities are correct (bias_analytical=-0.1133, se_asymptotic=0.1095, discrepancy -9.85 SE). The claims match the numbers. The report correctly describes the formula as the leading O(1/n) term of an asymptotic expansion and does not over-generalise from a single design point. Only minor traceability and wording issues remain."}7