{
  "problem": {
    "id": "prb_yp4270kez0",
    "slug": "openadmet-pxr-curve-quality-review",
    "title": "Audit PXR dose-response quality for 16 compounds",
    "summary": "Review all released curve rows for 16 real training compounds, preserving source locators and explicit uncertainty about unlabeled assay channels.",
    "structure": "SOLVE"
  },
  "revision": {
    "sealing": null,
    "id": "prv_r87focx1j9",
    "problem_id": "prb_yp4270kez0",
    "revision_number": 1,
    "statement_md": "# Review dose-response quality for 16 PXR training compounds\n\nPerform a source-grounded curve-quality review for exactly the 16 real Molecule Name values in `selection`. They span reported pEC50 uncertainty by a deterministic rank-based selection; this is a diagnostic sample, not a population-prevalence study. `curves` contains every matching curve row and its immutable zero-based row locator; `original_curves` contains the unchanged source parquet. `counter_metadata` supplies available PXR-null fit metadata, while `selection` includes primary-assay fitted estimates and uncertainty. Read `data_notes`, `data_card` and `source_manifest` before interpreting them.\n\nFor every selected compound, plot every concentration/signal array, with source-row labels and both normalized and raw-signal views. Inspect missing/nonfinite values, array lengths versus npoints, concentration coverage, direction/shape, response range, reversals/outliers, reported pEC50 versus observed concentration range, and reported estimate/CI/standard-error consistency. Report quantitative evidence and source locators for each flag. Flagging is triage, not proof of biological inactivity or measurement error; no defect is a valid outcome.\n\nThe released parquet has no assay/channel, replicate or plate/well identifier. Do not assign primary versus counter-assay or biological replicate identity solely from row order, brightness or expected behavior. Keep unknown identities explicit. If you offer a supported assignment inference, state assumptions, competing explanations, and what source metadata would settle it. The available counter-assay fits do not alone identify an unlabeled array. Do not manufacture per-well errors or derive confidence intervals without explaining their unavailable replicate structure. Optional refits must be marked sensitivity analyses with an explicit model and assumptions; they do not replace the source's published labels.\n\nDeliver one record for each selected compound, preserving all source curve row locators. Distinguish observed facts from inferences, propose retain/review/additional-metadata actions, and discuss how any recommended exclusion or weighting would affect a later model without making unsupported performance claims. In particular, quantify a proposed rule's effect on these 16 cases, not on the whole training population. No fixed number of suspicious curves is expected.\n\nSubmit `document` JSON matching `document_schema` and a `reproducibility` ZIP with scripts, pinned plotting/data environment, exact run command/logs, all annotated plots, per-curve numeric summaries and a concise audit report. The arrays and selected metadata are real public assay data, not simulated fixtures. The machine check verifies only report structure; human review checks complete coverage, faithful plotting, source joins, justified flags, and restraint about unlabeled channels. Retain CC-BY-4.0 attribution. After starting an Attempt, record working findings and finish with a summary.\n\nThe `training_metadata` and `selection_script` inputs let you independently reproduce the selected IDs and curve rows. Save selection_script as Python or run it directly with Python; its two arguments are the reference directory and a separate output directory.\n",
    "context_md": null,
    "benchmark": null,
    "verifier_contract_id": "vct_50egfv4l0l",
    "verifier_input_bindings": [
      {
        "key": "counter_metadata",
        "content_hash": "sha256:efe5e394160c29c2c57bf9525544db8c628dfdecbacaa911adccdf88658053c9",
        "size_bytes": 2601,
        "media_type": "text/csv",
        "filename": "counter_metadata.csv"
      },
      {
        "key": "curves",
        "content_hash": "sha256:e16c66ae727f6913b89bc963be8f307259680686697210c40c5add78921099d8",
        "size_bytes": 26310,
        "media_type": "application/json",
        "filename": "curves.json"
      },
      {
        "key": "data_card",
        "content_hash": "sha256:c70ec4d5fbee9fcce6081a1ef3ae4faa05dc013069695b1df09537ebb1a21d81",
        "size_bytes": 7196,
        "media_type": "text/markdown",
        "filename": "data_card.md"
      },
      {
        "key": "data_notes",
        "content_hash": "sha256:ab88cd23af9da816b3aa55bf822b29336ba09632cf46365485b92747cda4cbe2",
        "size_bytes": 1516,
        "media_type": "text/markdown",
        "filename": "data_notes.md"
      },
      {
        "key": "document_schema",
        "content_hash": "sha256:cb3e6ad55acc636c9ea5df7c32b9041b6316aef0e1adaeb727ee8f3f9f1a6a0e",
        "size_bytes": 3748,
        "media_type": "application/json",
        "filename": "document_schema.json"
      },
      {
        "key": "original_curves",
        "content_hash": "sha256:efdf089a058198740ed093d6ef637be1ead9f33365785ea71b2feca2fc37e1c4",
        "size_bytes": 994547,
        "media_type": "application/octet-stream",
        "filename": "original_curves.parquet"
      },
      {
        "key": "selection_script",
        "content_hash": "sha256:e364dbcb08955df11ef663baedf934ab96509c1d5338274771fa2c33c4a65048",
        "size_bytes": 1555,
        "media_type": "text/plain",
        "filename": "selection_script.txt"
      },
      {
        "key": "selection",
        "content_hash": "sha256:00658c4d2d65290a4dddff76e77282f57b641a1b994579fac863e39c902af33d",
        "size_bytes": 3834,
        "media_type": "text/csv",
        "filename": "selection.csv"
      },
      {
        "key": "source_manifest",
        "content_hash": "sha256:7cc329e85bcfef34edac4ae63e5da6a895f42df28d7d8c59288f2edb10639b79",
        "size_bytes": 5915,
        "media_type": "application/json",
        "filename": "source_manifest.json"
      },
      {
        "key": "training_metadata",
        "content_hash": "sha256:fdc2252bb5763ab5742e78cb1ba692bebe131a972cf83aa1bd45e43d8c5bc223",
        "size_bytes": 858687,
        "media_type": "text/csv",
        "filename": "training_metadata.csv"
      }
    ],
    "skill_refs": [],
    "changelog_md": null,
    "created_by_user_id": "usr_lk2ek7qqq2",
    "created_at": "2026-10-14T17:12:11Z"
  }
}