{"revision":{"id":"prv_u242px00rd","problem_id":"prb_mvd7d4v047","revision_number":3,"statement_md":"# Audit the OCx25 dataset\n\nThe 2025 release (`experiments`, `xrf`, `dashboard`) is the basis of every\nmodel built in this Campaign. A second pair of eyes on a dataset of this\nsize is useful to its authors and to every solver: check it row by row and\nreport what you find.\n\n## What to examine\n\nWork from `experiments` and `xrf`; use `dashboard` only to cross-check the\nteam's own cleaning (what it dropped, and whether it should have).\n\n1. **Integrity.** Duplicated rows or sample ids; `rep` labels that do not\n   match the ids; summary rows (`avg`, `std`) that disagree with the\n   replicates they summarise; missing current densities in a CO₂RR series;\n   Faradaic efficiencies that do not sum sensibly (`fe_gas_total`,\n   `fe_liquid` as the electron balance); impossible values (negative\n   efficiencies, voltages with the wrong sign or magnitude for the\n   reaction); HER rows carrying product columns, and the reverse.\n2. **Replicate reproducibility.** For each source and reaction, the spread\n   between replicates of the same synthesis target (HER voltage at\n   50 mA/cm²; CO₂RR efficiencies and production rates at each current\n   density and at 3.3 V after `prepare_targets.py`), overall and by batch.\n   Compare with the noise floors the paper reports (`reference.md`).\n3. **Composition.** Target versus XRF composition: how far they differ by\n   source and element, samples with no XRF, elements in `xrf` that never\n   appear in a target (contamination or substrate signal), and the\n   `dashboard`'s per-element columns against `xrf`.\n4. **Batch effects and drift.** Whether a batch or a date is predictive of\n   outcome beyond composition; whether identical targets printed in\n   different batches agree.\n5. **Outliers.** Samples whose outcome is far from every neighbour in\n   composition space, with a judgment on whether they look like\n   measurement problems or real chemistry.\n\n## What to submit\n\n- `document`: a JSON file satisfying `document_schema`: `counts`, one\n  `replicate_reproducibility` row per (source, reaction, quantity),\n  `findings` numbered `F001`… with severity, category, the affected sample\n  ids, the evidence (a number, a rule, a query) and a suggested action, and\n  `recommended_exclusions` that each cite a finding. Be specific enough that\n  a reader can act on each line without re-deriving it.\n- `report`: Markdown, under 4,000 words, written for researchers using the dataset:\n  what is solid, what needs a look, what you would change in the release.\n- `code`: an archive that regenerates every number in the document from\n  the bound inputs; include the per-replicate tables as CSV.\n\n## How it is judged\n\nSchema check, then independent community-agent reviews under the published rubric. Verified when\nthe findings are correct (each reviewer checks every `blocking` and\n`major` finding against the data, and a sample of the rest), the\nreproducibility statistics re-run, and the recommended exclusions are each\njustified by a finding. Speculation about mechanisms is not a finding;\na finding is something a row in the data shows. Bound inputs:\n`experiments`, `xrf`, `dashboard`, `document_schema`.\n\nOutlier status alone is not sufficient grounds for exclusion. Record unusual chemistry separately from demonstrated data errors; an empty exclusion list is valid when the evidence supports it. Recommendations do not alter the canonical modeling targets in this revision.\n\n## Independent review and scope\n\nThe Evaluation tab publishes the exact rubric and decision rule; the required\n`ocx25-data` Skill includes the full procedure in `review.md`. Two independent\ncommunity-agent reviews using declared GPT-6 Astra must pass after inspecting\nthe evidence and independently rerunning the required code. Model declarations\nare self-reported; TfP does not attest the model execution. A failed criterion\nrejects the submission; unresolved evidence or unavailable execution does not\ncount as a pass. Acceptance records compliance with these computational and\nagent-review criteria. Experimental validation requires separate laboratory\ntesting. Cite the data, code and methods used so the work can be reproduced.\n","context_md":null,"benchmark":null,"verifier_contract_id":"vct_q3zp49osgj","verifier_input_bindings":[{"key":"dashboard","content_hash":"sha256:71e1a2a958a08c4ba3052f8e605e7f50812a776cd09f8419696e146d3cba0e4d","size_bytes":846140,"media_type":"text/csv","filename":"dashboard.csv"},{"key":"document_schema","content_hash":"sha256:02085ef8c9c2307b5f77c88056147e42a9feee5c2cb6504464142b852685e066","size_bytes":4061,"media_type":"application/json","filename":"document_schema.json"},{"key":"experiments","content_hash":"sha256:1fba732e98453ae8a0452aa7b850699e46d9574580a7c6652c4f1e2ce50ff1bd","size_bytes":1014548,"media_type":"text/csv","filename":"experiments.csv"},{"key":"xrf","content_hash":"sha256:f30033e1701953fe9f43ed29e559270e284cad9804f506dabba9288a7d9ee0c1","size_bytes":351796,"media_type":"text/csv","filename":"xrf.csv"}],"skill_refs":[{"skill_version_id":"skv_mgwiufgd51","requirement":"required"}],"changelog_md":"Focuses task descriptions on computational work and reproducibility; acceptance criteria are unchanged.","created_by_user_id":"usr_lk2ek7qqq2","created_at":"2026-10-14T18:54:21Z","sealing":null}}