[codex] add overall benchmark evaluation report

[jmchandonia]

Summary

• add a cross-fold evaluation report summarizing all 13 Codex benchmark runs
• identify common failure modes across datasets: hidden row/site inclusion policy, site-id canonicalization, wide-to-long parsing, interval policy, sentinel/gold defects, and brittle JSON literal matching
• explain which failures are clear with gold access and which reflect missing task specification rather than simple agent mistakes

Key Conclusions

• all sub-agent traces passed the targeted local no-gold path audit
• output failures clustered around hidden curation decisions and exact key canonicalization, not just pandas coding errors
• several gold decisions were not recoverable from fold-local skills/raw files, including site inclusion choices, treatment/site policies, manuscript-derived constants, and some questionable gold interval/sentinel behavior
• future scoring should separate row-inclusion, value-transform, site-id canonicalization, semantic JSON quality, and known gold-defect handling

Validation

• report compiled from the per-fold evaluation PR metrics and targeted gold-code inspection
• documentation-only change

[claude]

Claude finished @jmchandonia's task —— View job

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PR Review

• Read CLAUDE.md for repo-specific guidelines (no CLAUDE.md found)
• Read the evaluation document
• Provide review feedback

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This is a documentation-only PR adding a cross-fold benchmark evaluation report. Overall, the document is well-structured and provides clear, actionable analysis. Here is my detailed feedback:

Strengths

• The PR Index table (evaluations/OVERALL_BENCHMARK_EVALUATION.md:14-28) is a clean and useful summary, though see the concern about the metrics below.
• The six failure modes (lines 48-53) are well-categorized and systematically addressed. This is a useful taxonomy for guiding future benchmark improvements.
• Recommendations (lines 252-282) are specific, concrete, and directly tied to observed failure modes — particularly the suggestion to score row inclusion and value transformation separately.
• The distinction between "gold equivalence failures" and "scientifically correct behavior" (e.g., the interval calculation example in fold 05) is an important nuance that is clearly articulated.

Issues and Suggestions

1. PR Index table: column header ambiguity (line 14)

Key precision and Gold recall are not explained inline or in a legend. A reader unfamiliar with the scoring methodology will not know whether these are micro/macro averages, what the key space is, or which metric file produces them. Consider adding a brief note or linking to the relevant metric definition (e.g., src/metrics/skill2_output_equiv.py).

2. Zero-score folds need more nuance (lines 22-28)

Five folds have 0.000 / 0.000 precision/recall. While the per-fold analysis (lines 233-246) explains these, the table itself with five zeros in a row could mislead a reader into thinking the benchmark was mostly unsuccessful. A short note in the "Big Picture" section clarifying that zero keyed overlap ≠ zero useful output would help.

3. Fold 02 / index 26 is not discussed individually (line 17)

Fold 02 covers indices 15 and 26, but the fold-level section (line 232) only mentions "Dataset 15 is especially brittle." There is no explanation of what happened with dataset 26 specifically. Given that the pair scored 0.000 / 0.000, both deserve a mention.

4. Sentinel value list is incomplete (lines 145-148)

The document lists indices 1, 8, 9, and 16 as cleaning -9999. However, some gold modules handling this may not be enumerated here. Since you have access to the expert code (data/gold/expert_code/harmonize_sm/), it would strengthen this section to confirm whether any other dataset files also perform sentinel cleanup that is missing from this list.

5. "Arguable" gold behavior should be flagged more precisely

The report mentions "questionable gold interval/sentinel behavior" in two places (lines 210 and 156), but doesn't link to or quote the specific gold code that exhibits this issue. For reproducibility and future developer reference, consider referencing the specific dataset file(s) and line numbers (e.g., data/gold/expert_code/harmonize_sm/dataset_05.py).

6. Missing aggregate statistics

The report never summarizes aggregates such as mean/median precision and recall across folds, or what fraction of folds fall into each failure mode category. Even a one-line summary (e.g., "5 of 13 folds scored zero keyed overlap; 3 of those are attributable solely to canonicalization") would make the "Bottom Line" section more quantitative.

7. Minor: Inconsistent fold reference style

The report uses "Fold 03 / index 4" (section headers) and "fold 03 / index 4" (inline prose). Consistency on capitalization would improve readability.

8. The evaluations/ directory is otherwise empty

This document is the only file in evaluations/. It may be worth noting whether per-fold evaluation reports live in the individual PRs only, or whether they should eventually be stored here. The current structure may cause confusion for future contributors.

Security / Test Coverage

No code is added or modified — this is a documentation-only change. No security concerns and no test coverage applies.

Summary

The document is substantive and provides genuine insight into benchmark failure patterns. The core analysis is sound. The main improvements would be: clarifying metric definitions, adding aggregate statistics, and filling in the missing fold 02/index 26 analysis.