1{"verdict":"accept",2"issues":[3{"id":"R1","severity":"minor","location":"out/claims.json C6; results.json sd/se keys","issue":"C6 is a hypothetical ('if treated as a sample') SE, and the claims file marks it 'supported'. The report itself says the estimands describe the file exactly, so this is not really a central claim. The extra keys sd_revenue_musd and se_mean_revenue_musd are not required, though they do no harm.","suggested_fix":"Mark C6 'not_evaluated' or drop it, and keep the SE only as an illustration in the Uncertainty section."},4{"id":"R2","severity":"minor","location":"out/report.md Uncertainty vs Limitations","issue":"The Uncertainty section says missing revenue is 'treated as missing completely at random'. Limitations then says excluding those firms may bias the aggregates. Both are reasonable, but the first wording reads as an assumption the analysis relies on, when it is really an untested caveat.","suggested_fix":"Rephrase: 'unknown revenues are excluded, not imputed; aggregates are unbiased for the full file only if missingness is unrelated to revenue.'"}5],6"summary":"Re-running analysis.py in a scratch copy reproduces results.json, analytic.csv and exclusions.csv byte-for-byte. Independent checks confirm: 445 rows; 45 exact full-row duplicates leaving 400 distinct firm_ids, with no conflicting duplicates; 7 empty revenue cells in raw rows, which become 6 distinct firms after dedup; 394 analysed firms; 60 distinct firms in USD_thousands, correctly divided by 1000; revenue per employee after conversion is similar for both unit groups; the raw baseline mean is 30695.88 over the 438 non-empty raw rows; and mean employees over the 400 deduplicated firms is 1101.435. The ledger arithmetic chains correctly from 445 to 394. Only data/firms.csv is used, and the code has no forbidden inputs or look-ahead. Every required key is present, and the claims match the numbers. Only minor wording issues."}7