{"claim":{"id":"d381306a-5dc9-4ca3-8ce1-5827409b47f2","submission_id":"176274b2-03a7-440e-ac13-afb57e905c38","submission_revision_id":"2c390daf-9d55-426b-9ee7-ca0d15309a8e","builder":"builder-01","status":"active","claimed_at":"2026-10-15T14:58:07.226938Z","delivered_at":null,"ended_at":null,"delivery_note":null,"end_reason":null},"task":{"submission_id":"176274b2-03a7-440e-ac13-afb57e905c38","submission_revision_id":"2c390daf-9d55-426b-9ee7-ca0d15309a8e","revision_version":6,"submission_status":"under_review","submitted_at":"2026-08-14T22:35:21.165134Z","submitter_name":"Tuan Vinh","submitter_email":"vinhtb@nih.gov","task":{"status":"submitted","version":6,"extra_fields":{"difficulty_self_test":{"model":"GPT 5.6 Sol Extra High","harness":"Codex","estimated_score":"10–20%"}},"submitted_at":"2026-08-14T22:35:21.165134+00:00","industry_domain":"Chemistry - Cheminformatics and Scientific Data Extraction","task_short_name":"chemistry_paper_id_2_extract_001","operating_system":"Windows, Linux, MacOS","task_description":"Objective: Read the attached original journal article and supplementary-information PDF and create an Excel workbook containing every reported CP50 measurement, the corresponding molecule, a stereochemistry-preserving molecular structure, and the complete experimental conditions associated with that measurement.\n\nStarting State: The complete required inputs are the two uploaded files `ja8b06144.pdf` and `ja8b06144_si_001.pdf`. The DOI `10.1021/jacs.8b06144` identifies the article but is not an additional input. Examine the two supplied PDFs completely, including the main text, methods, tables, figures, captions, schemes, footnotes, and supplementary tables. Do not use an online copy of the article, an external database, or an external reference annotation as a substitute for the supplied PDFs.\n\nDeliverable: Create one workbook named `Output.xlsx`. It must contain exactly one worksheet named `Data` and exactly the following eight columns in this order:\n\n`CP50 Values`, `Paper ID`, `Molecule`, `Final_Canonical_SMILES`, `Cell_Type`, `Medium`, `Pulse`, `Chase`\n\nThe expected dataset contains 115 measurement rows. Row order is not graded. Do not sort or deduplicate rows merely because two measurements have identical displayed values. Preserve repeated records when the PDFs report them as separate assay measurements or separate experimental comparisons. When the same graphical result is merely reproduced as an inset or visual summary without representing an additional assay record, include it only once.\nSet `Paper ID` to numeric value `2` in every data row. ### CP50 Values\n\nRecord both the reported point estimate and its reported uncertainty using this exact serialization:\n\n`estimate +/- uncertainty uM`\n\nExamples: `0.11 +/- 0.01 uM`, `5 +/- 1 uM`, and `2800 +/- 1500 uM`.\n\nUse the numerical precision shown in the source entry. The prescribed `+/-` and `uM` output spellings take precedence over alternative typography such as `±`, `µM`, or `μM` in the PDFs. Do not omit the uncertainty.\n\n### Molecule\n\nUse the molecule or construct label associated with the measurement in the relevant figure or table. Preserve distinctions among constructs, linker variants, alanine variants, fusion peptides, and positional isomers. Do not assign one molecule's CP50 value or structure to another molecule.\n\n### Final_Canonical_SMILES\n\nRepresent the complete assayed construct using a valid canonical isomeric SMILES. Preserve connectivity, formal charge, isotopes, all disconnected components, and every stereochemical feature explicitly defined by the source, including L/D residue identity and defined stereochemistry in nonpeptide components. Do not remove salts, retain only the largest fragment, or return a non-isomeric SMILES when the source defines stereochemistry. Chemically equivalent valid isomeric SMILES are acceptable because evaluation canonicalizes structures with RDKit.\n\n### Experimental-condition encoding\n\nPopulate conditions from the measurement's figure or table and its governing caption or method. A condition does not need to be printed beside every numerical value when the caption or assay method explicitly governs the entire panel or table.\n\nUse exactly one of these `Cell_Type` values:\n\n- `HeLa HaloTag-GFP-Mito (cytosolic)`\n- `HeLa H3-GFP-HaloTag (nuclear)`\n\nUse exactly one of these `Medium` values:\n\n- `Opti-MEM`\n- `DMEM (no serum)`\n- `DMEM + 10% FBS`\n\nUse exactly one of these `Pulse` values:\n\n- `0.5 h at 37 °C with 5% CO2`\n- `2 h at 37 °C with 5% CO2`\n- `4 h at 37 °C with 5% CO2`\n- `8 h at 37 °C with 5% CO2`\n- `24 h at 37 °C with 5% CO2`\n- `4 h at 4 °C`\n\nUse exactly this `Chase` value:\n\n- `15 min with 5 µM ct-TAMRA`\n\nUnless a figure, table, or caption specifies another condition, use the default CAPA conditions established by the methods: `HeLa HaloTag-GFP-Mito (cytosolic)`, `Opti-MEM`, `4 h at 37 °C with 5% CO2`, and `15 min with 5 µM ct-TAMRA`.\n\nFor the serum comparison, encode the medium as `DMEM (no serum)` or `DMEM + 10% FBS`. For the temperature comparison, encode the complete temperature-specific pulse, including `4 h at 4 °C` for the cold condition. For the compartment comparison, use the specific nuclear or cytosolic reporter cell line. For the time-course measurements, preserve the individual pulse duration associated with each CP50 value.\nDo not place temperature, serum status, compartment, or reporter concentration only in an ungraded comment, cell note, formatting property, or additional worksheet. Encode them in the required fields exactly as specified above.","software_licensing":"Free/Open Source","evaluation_criteria":null,"verification_method":"Parse the submitted workbook and reference workbook as `.xlsx` files.\n\n### Workbook structure\n\nThe submitted file must be named `Output.xlsx`. It must contain exactly one worksheet named `Data`. That worksheet must contain exactly eight columns in this order and with these exact headers:\n\n`CP50 Values`, `Paper ID`, `Molecule`, `Final_Canonical_SMILES`, `Cell_Type`, `Medium`, `Pulse`, `Chase`\n\nThe corrected reference contains 115 data rows. Ignore entirely empty trailing worksheet rows. Missing rows are false negatives and extra nonempty rows are false positives. An additional worksheet, missing worksheet, renamed worksheet, missing or extra column, duplicate header, or malformed workbook is a structural error and prevents a passing result, although row and cell diagnostics should still be reported when the workbook can be parsed safely.\n\n### Paper ID\n\nTreat numeric `2` and string `2` as equivalent. Every submitted data row must have `Paper ID` equal to `2`. Any other value is incorrect.\n\n### Text normalization\n\nFor `Molecule`, `Cell_Type`, and `Medium`, apply Unicode NFKC normalization, trim leading and trailing whitespace, collapse each internal whitespace run to one space, and apply Unicode case folding. Do not otherwise replace words, punctuation, construct names, abbreviations, serum descriptions, or biological labels.\n\nAfter this normalization, `Cell_Type` and `Medium` must match one of the controlled values specified in the task description. An unspecified `HeLa` value does not match either compartment-specific reporter cell line. `DMEM` alone does not match either defined serum condition.\n\n### SMILES normalization\n\nUse RDKit 2024.03.1 for both submitted and reference structures. Parse each value with `Chem.MolFromSmiles` using sanitization. Clear atom-map numbers, remove only RDKit-removable explicit hydrogens with `Chem.RemoveHs`, and generate canonical isomeric SMILES with:\n\n```python\nChem.MolToSmiles(mol, canonical=True, isomericSmiles=True)\n```\n\nPreserve formal charge, isotopes, disconnected components, and defined stereochemistry. Do not strip salts or retain only the largest fragment. Two SMILES values match when their resulting canonical isomeric SMILES are identical. A blank or invalid submitted SMILES cannot match a valid reference structure. A connectivity match with missing, inverted, or additional defined stereochemistry is not a structure match.\n\n### CP50 normalization\n\nEach `CP50 Values` cell must contain a point estimate, an uncertainty separator, an uncertainty, and a concentration unit. Parse the grammar:\n\n`estimate (\"+/-\" or \"±\") uncertainty unit`\n\nAccept `uM`, `µM`, and `μM` as equivalent micromolar spellings. Also recognize `nM` and `mM`. Convert both the estimate and the uncertainty to micromolar using decimal arithmetic. A unitless CP50 value is invalid because all reference measurements have defined concentration units and uncertainties.\n\nTwo CP50 cells match only when both converted point estimates and both converted uncertainties are numerically equal. Decimal forms such as `5`, `5.0`, and `5.00` are numerically equivalent. Do not discard the uncertainty, compare only the point estimate, or treat the entire CP50 cell as an unparsed text label.\n\n### Pulse normalization\n\nParse `Pulse` as a complete condition tuple containing:\n\n- duration in minutes;\n- temperature in degrees Celsius; and\n- CO2 percentage when stated.\n\nRecognize equivalent singular, plural, and abbreviated time units and convert seconds, minutes, or hours to minutes. Recognize equivalent degree symbols and case-insensitive spellings of `CO2`. Numerically equivalent duration, temperature, and CO2 forms may match, but all components present in the reference must match. A pulse with the correct duration but the wrong or omitted temperature or CO2 condition does not match.\n\n### Chase normalization\n\nParse `Chase` as a tuple containing the chase duration, reporter identity, and reporter concentration. Convert the duration to minutes and the concentration to micromolar. Normalize the reporter label using NFKC, whitespace normalization, and case folding. A chase matches only when its duration, `ct-TAMRA` identity, and `5 µM` concentration all match.\n\n### Row matching and scoring\n\nCompare submitted and reference rows as order-independent multisets using maximum one-to-one matching. Duplicate rows remain significant, and one submitted row cannot match more than one reference row. A full row match requires all eight normalized fields to match.\n\nLet:\n\n- `TP` be the number of fully matched rows;\n- `FP` be the number of unmatched submitted rows; and\n- `FN` be the number of unmatched reference rows.\n\nReport:\n\n- row precision: `TP / (TP + FP)`;\n- row recall: `TP / (TP + FN)`; and\n- row F1: `2TP / (2TP + FP + FN)`.\n\nRow F1 is the primary score.\n\nAlso report cell-level partial credit. Use maximum-weight one-to-one assignment based on the number of equivalent normalized fields in each possible row pair. Unmatched rows receive zero correct cells. Compute cell accuracy as:\n\n`equivalent assigned cells / (8 * max(115, submitted_row_count))`\n\nThis diagnostic awards partial credit to nearly correct rows but does not convert them into row-level true positives.\n\nA passing submission requires:\n\n- a structurally valid workbook;\n- exactly one worksheet named `Data`;\n- the exact eight-column schema;\n- no duplicate column headers;\n- row recall of at least `0.95`; and\n- row F1 of at least `0.95`.\n\nReport the following diagnostics: TP, FP, FN, submitted row count, row precision, row recall, row F1, cell accuracy, invalid SMILES count, stereochemistry mismatch count, malformed CP50 count, incorrect Paper ID count, uncontrolled condition-value count, and structural workbook errors.\nScores below the passing thresholds retain their measured row-level and cell-level partial credit; evaluation is not all-or-nothing.","software_and_version":"Python 3.11; RDKit 2024.03.1; pandas 2.2; openpyxl 3.1","specific_requirements":null,"expected_completion_time":null},"files":[{"file_id":"c0a28c96-035b-4a80-876a-1897e52548e4","filename":"ja8b06144_si_001.pdf","category":"input_materials","content_type":"application/pdf","size_bytes":3584058},{"file_id":"688a7b6d-29fe-4813-82e4-6f15bdc170f5","filename":"ja8b06144.pdf","category":"input_materials","content_type":"application/pdf","size_bytes":3086670},{"file_id":"161234ef-70f6-4fcd-ad97-0a3096fc921f","filename":"Output.xlsx","category":"reference_output","content_type":"application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","size_bytes":10769}],"deep_review":{"overall_verdict":"Borderline Accept","review_text":"**Overall Verdict:** Borderline Accept — The task is professionally scoped and evidence-complete, but exact `Molecule` matching is materially ambiguous because the same measurements use conflicting labels across figures and tables.\n\n**Completeness Check:**\n- Instruction: Unclear — It does not establish precedence or accepted aliases when figures use `ct-Arg9` and `ct-SAHB` while the corresponding tables and reference use `ct-R9W`, `ct-SAHB_A1`, or `ct-BIM-SAHB_A1`.\n- Inputs: Complete — Both authentic, readable PDFs were supplied and cover the article, structures, sequences, assay methods, figures, captions, and seven supplementary tables.\n- Toolchain: Complete — Python, RDKit, pandas, and openpyxl are legally obtainable, appropriate, and compatible with a single-machine workflow.\n- Reference output: Complete — The workbook is valid OOXML with one `Data` worksheet, the exact eight-column schema, 115 populated rows, and coverage reconciling to all reported measurement groups.\n- Rubric: Unclear — Exact normalized molecule-label matching does not accommodate the source’s aliases or subscript serialization, even though six alias mismatches alone would reduce recall below the 0.95 passing threshold.\n\n**File and Task Reachability:**\nThe attachment set is `reachable`: `ja8b06144.pdf` was inspected as an unencrypted 10-page PDF, `ja8b06144_si_001.pdf` as an unencrypted 20-page PDF, and `Output.xlsx` as a valid, non-macro OOXML workbook. All manifest attachments were reviewed; no embedded files, scripts, external workbook relationships, or access barriers were found. Exact RDKit sanitization and stereochemistry comparison could not be independently rerun because a compatible chemistry runtime was unavailable in the review environment, which is a reviewer-tooling limitation rather than a submission defect. The task is `conditionally_reachable`: extraction, structure construction, workbook production, and objective evaluation are feasible once molecule-label precedence or alias handling is clarified. License keys, accounts, tokens, and other credentials are not required from the contributor. Confidential, proprietary, or internal material may be evaluated as submitted evidence without requiring additional disclosure.\n\n**Special Screening:**\nNone. The DOI identifies a public journal article, but the submitted evidence does not establish a public benchmark or publicly available protected answer; external search was not used.\n\n**Recommended Changes:**\nThe source coverage, reference workbook, controlled conditions, and scoring design are otherwise strong.\n\n- Define an authoritative molecule-label vocabulary or state that supplementary-table labels take precedence over figure labels.\n- Alternatively, update evaluation normalization to accept source-equivalent aliases such as `ct-Arg9`/`ct-R9W` and `ct-SAHB`/`ct-SAHB_A1`/`ct-BIM-SAHB_A1`, including equivalent subscript and underscore serialization.","reviewed_at":"2026-08-14T22:44:19.498349Z"},"claim":{"id":"d381306a-5dc9-4ca3-8ce1-5827409b47f2","submission_id":"176274b2-03a7-440e-ac13-afb57e905c38","submission_revision_id":"2c390daf-9d55-426b-9ee7-ca0d15309a8e","builder":"builder-01","status":"active","claimed_at":"2026-10-15T14:58:07.226938Z","delivered_at":null,"ended_at":null,"delivery_note":null,"end_reason":null}},"revision_is_current":true}