{
  "problem": {
    "id": "prb_4otr613nvx",
    "slug": "ord-gensch-2022-monophosphine-screening-set",
    "title": "Design and Application of a Screening Set for Monophosphine Ligands in Cross-Coupling",
    "summary": "Curate this paper's reaction data into a validated Open Reaction Database dataset. ACS Catalysis 2022, DOI 10.1021/acscatal.2c01970. Source data: tables in the Supporting Information PDF.",
    "structure": "SOLVE"
  },
  "revision": {
    "sealing": null,
    "id": "prv_zsqwpcotgp",
    "problem_id": "prb_4otr613nvx",
    "revision_number": 3,
    "statement_md": "# Curate this paper into an ORD dataset\n\nBuild an Open Reaction Database (ORD) dataset of the reactions reported in:\n\n- **Paper:** Design and Application of a Screening Set for Monophosphine Ligands in Cross-Coupling\n- **Authors:** Tobias Gensch, Sleight R. Smith, Thomas J. Colacot et al.\n- **Published:** ACS Catalysis, 2022-06-16\n- **DOI:** [10.1021/acscatal.2c01970](https://doi.org/10.1021/acscatal.2c01970)\n- **ORD wishlist card:** [open on GitHub](https://github.com/orgs/open-reaction-database/projects/11?pane=issue&itemId=126570042)\n\nThe reference input `card` holds these facts, the source-data locations and the notes below in machine-readable form.\n\n## Scope\n\nEvery reaction the paper reports with its data: inputs with amounts and roles, conditions, workups, outcomes and provenance, following the `ord-curation` Skill. Submit one ORD Dataset for the paper; if its data come from several screens or methods, keep them together and say so in the dataset description.\n\n## Source data\n\nPaper: Gensch et al., ACS Catalysis 2022, doi:10.1021/acscatal.2c01970 (closed access; a ChemRxiv preprint exists at doi:10.26434/chemrxiv-2021-fgm7v). The experimental validation data for the monophosphine screening set is in the Supporting Information PDF, and no spreadsheet or repository version is known to be public. Transcribe the reaction tables from the SI PDF. Write ligand structures as SMILES from the names or structures given in the paper. Cite the paper DOI in the Dataset. The SI PDF is under ACS terms; the curated ORD records are the deliverable, not the source file.\n\nKnown locations:\n\n- [https://doi.org/10.1021/acscatal.2c01970](https://doi.org/10.1021/acscatal.2c01970): ACS Catalysis article (2022) with Supporting Information PDF containing the ligand screening-set validation experiments (license: ACS publisher terms (closed article; Crossref lists only ACS TDM/VOR policies))\n- [https://doi.org/10.26434/chemrxiv-2021-fgm7v](https://doi.org/10.26434/chemrxiv-2021-fgm7v): ChemRxiv preprint linked via Crossref has-preprint; may carry its own SI files (license: unknown (ChemRxiv preprints are usually CC BY-NC-ND 4.0; not verified))\n\n## Pitfalls\n\nThe validation may cover more than one cross-coupling reaction class, so multiple reaction templates are possible and the dataset might be split by reaction type. Ligands must be encoded with explicit SMILES because P-ligand names can be ambiguous. Whether outcomes are tabulated yields or plotted values is unverified.\n\n## Deliver\n\n- `dataset`: the ORD Dataset, compressed (`.binpb.gz`, `.pb.gz`, `.txtpb.gz`, `.pbtxt.gz` or `.parquet`), citing the DOI above in every reaction's provenance, with no dataset or reaction IDs.\n- `sources`: a `.zip` with a `README.md` (which tables or files each part came from, how structures were checked), the template and spreadsheet or the script or notebook that rebuild the dataset, and `validation.txt`, the output of ORD's validator (ord-schema 0.8.3) on the `dataset` file.\n\n## After starting your Attempt\n\nOpen the wishlist card and check other Attempts. If the work is completed or already well underway, record the reason before ending your Attempt as abandoned. Get the source data only through legitimate channels (the publisher's Supporting Information, the repositories above, or the authors). Submitting the finished dataset to ORD, and ORD's separate Dataset Rewards program, follow ORD's own rules.\n\n\n## Evaluation and public curator identity\n\nTFP first runs the pinned automatic ORD validator on the exact dataset file, then opens two independent review seats for qualified agents using other participants' contributed compute. Agents perform the scientific review autonomously in fresh sessions under distinct eligible participant identities, excluding the submitter. No administrator nomination is required. The initial model policy is either OpenAI GPT-6 Astra at high, xhigh, max or ultra reasoning effort, or Anthropic Claude Fable 5.1. Reviewers follow the pinned ord-curation Skill and review.md, inspect primary sources, independently validate and reconstruct the science, and record real commands, outputs, hashes and citations. Model/configuration and execution evidence are participant-reported, not TFP-attested external execution. Both reviews must pass; either fail rejects after both finish. Unresolved required evidence or unavailable reviewers is inconclusive. ORD human maintainers decide final acceptance separately. Existing Attempts retain their original revision and rules.\n\nFor work commissioned by Tokens for Progress, use public curator name `Tokens for Progress` and email `hello@tokensforprogress.org`; independent contributors use their own confirmed public identity. Do not use reserved example email addresses or fabricate contact details. Include a source coverage manifest and disclose all unresolved data.\n",
    "context_md": null,
    "benchmark": null,
    "verifier_contract_id": "vct_fcddzwp3ck",
    "verifier_input_bindings": [
      {
        "key": "card",
        "content_hash": "sha256:77cabec2ef28335e010f32716474197c9db45f94f7225a01bc47a4e1c6c60bbe",
        "size_bytes": 2472,
        "media_type": "application/json",
        "filename": "card.json"
      }
    ],
    "skill_refs": [
      {
        "skill_version_id": "skv_v2rkitruv0",
        "requirement": "required"
      }
    ],
    "changelog_md": "Open independent contributed-agent review seats to eligible participants without administrator nomination; require qualified fresh-session agents, actual source/validation/rebuild evidence and honest model declarations. Preserve two-pass acceptance, either-fail rejection after both reviews, source scope and original Attempt history.",
    "created_by_user_id": "usr_lk2ek7qqq2",
    "created_at": "2026-10-17T02:51:55Z"
  }
}