{"claim":{"id":"7b4a8097-030f-4761-aacf-df7f1c4af924","submission_id":"33ea1e9f-bdf5-4bce-a2cd-0e4021e95612","submission_revision_id":"fcb52bf2-8211-4c35-a12a-06acd5d6bf0e","builder":"builder-01","status":"active","claimed_at":"2026-10-15T14:58:24.384252Z","delivered_at":null,"ended_at":null,"delivery_note":null,"end_reason":null},"task":{"submission_id":"33ea1e9f-bdf5-4bce-a2cd-0e4021e95612","submission_revision_id":"fcb52bf2-8211-4c35-a12a-06acd5d6bf0e","revision_version":4,"submission_status":"under_review","submitted_at":"2026-08-15T02:27:39.601866Z","submitter_name":"Christian Hoang","submitter_email":"christianhoang04@gmail.com","task":{"status":"submitted","version":4,"extra_fields":{"drive_link":"N/A","difficulty_self_test":{"model":"GPT 5.6 Sol High","harness":"None / direct model session","estimated_score":"<1%"}},"submitted_at":"2026-08-15T02:27:39.601866+00:00","industry_domain":"Biomedical Research / Bioinformatics","task_short_name":"preclinical_pico_exact_span_extraction","operating_system":"Windows","task_description":"Objective: Extract exact character spans for six PICO entity categories from 20 difficult preclinical in-vivo animal-study abstracts: Species, Strain, Induction, Intervention, Comparator, and Outcome.\n\nStarting State: The agent receives pico_span_extraction.xlsx containing 20 abstracts with opaque case IDs, pico_cases.jsonl containing the exact source strings used for character indexing, TASK_INSTRUCTIONS.md, and SPAN_FORMAT_GUIDE.md.\n\nInstructions and Rules: For every case, identify every source-supported exact span belonging  to the six PICO categories. Focus on the actual in-vivo animal experiments. Distinguish experimental induction/model manipulation from intervention, comparator, and strain roles. Exclude interventions performed only in isolated cells or tissues in vitro or ex vivo. Preserve repeated eligible mentions at separate offsets. Use the shortest meaningful phrase, avoid overlapping spans, and do not infer information missing from the supplied abstract.\n\nEach span must be represented as JSON with a 0-based, end-exclusive start and end offset plus the exact source text. Every span must satisfy abstract_text[start:end] == text. Sort spans by start and then end. Fill all six span columns for every case, including empty JSON lists where no span applies. Also fill confidence as HIGH, MEDIUM, or LOW and provide a concise extraction note of at least five words.\n\nOutput: Save the completed workbook as pico_span_extraction_output.xlsx.\n\nExpected Completion Time: Approximately 3–5 hours for an experienced biomedical evidence annotator; longer for a non-specialist.","software_licensing":"Free/Open Source","evaluation_criteria":null,"verification_method":"The evaluator must compare the completed workbook with the hidden expert reference by opaque  case_id.\n\nFor each of the 20 cases, compare the six PICO label columns as sets of exact tuples: (label, start_char, end_char).\n\nA predicted span receives credit only when its label, start offset, and end offset exactly match a hidden reference span. The supplied text must satisfy: abstract_text[start_char:end_char] == text\n\nCompute:\n  - 60% macro-F1 across the six labels;\n  - 40% micro exact-span F1 across all entities.\n\nApply structural completeness gating. The output must:\n  - contain all 20 case IDs exactly once;\n  - preserve every input abstract_text exactly;\n  - provide valid JSON lists in all six span columns;\n  - use valid 0-based, end-exclusive offsets;\n  - provide confidence as HIGH, MEDIUM, or LOW;\n  - provide an extraction note of at least five words for every case;\n  - contain no invalid spans or duplicate case IDs.\n\nMissing or invalid core spans must not be rescued by confidence or note fields. A perfect pass requires exact reproduction of every hidden reference span for all 20 documents and complete structural validity. The final score must be deterministic and range from 0 to 1.","software_and_version":"  LibreOffice Calc or Microsoft Excel; Python 3 for local span validation and JSON handling","specific_requirements":null,"expected_completion_time":null},"files":[{"file_id":"7d80e5ff-3c3c-4f17-a847-f95a4aaecf70","filename":"SPAN_FORMAT_GUIDE.md","category":"input_materials","content_type":"text/markdown","size_bytes":279},{"file_id":"b40b39f9-3818-45cd-9900-5a027929b347","filename":"pico_cases.jsonl","category":"input_materials","content_type":"application/octet-stream","size_bytes":35504},{"file_id":"e3ba65ea-b793-4f91-a33e-3c79742a7066","filename":"TASK_INSTRUCTIONS.md","category":"input_materials","content_type":"text/markdown","size_bytes":1944},{"file_id":"67aa2cbf-70ea-412f-b884-a8134ec155cc","filename":"pico_span_extraction.xlsx","category":"input_materials","content_type":"application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","size_bytes":18578},{"file_id":"816d4913-7819-418b-a706-4ad24dab58dd","filename":"hidden_entities.csv","category":"reference_output","content_type":"text/csv","size_bytes":74241},{"file_id":"1cffa388-d661-454c-8ed8-c37120e12aaa","filename":"hidden_reference.xlsx","category":"reference_output","content_type":"application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","size_bytes":24977},{"file_id":"fb8c7c65-c1a9-4db4-b1a6-fee671792602","filename":"score_pico_submission.py","category":"reference_output","content_type":"application/octet-stream","size_bytes":8306},{"file_id":"8305e83f-f14f-483f-afe4-67b4fa358829","filename":"case_texts.jsonl","category":"reference_output","content_type":"application/octet-stream","size_bytes":35504},{"file_id":"f3ccee05-d51f-43e1-9fe4-6df0ff957c82","filename":"pico_span_extraction_output.xlsx","category":"difficulty_self_test_evidence","content_type":"application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","size_bytes":24735},{"file_id":"e2f1ac9b-b37b-418d-8c9d-42829d184ec2","filename":"evidence_checking_on_another_5.6-high.jpg","category":"difficulty_self_test_evidence","content_type":"image/jpeg","size_bytes":105868},{"file_id":"c0b1cc41-7a1e-436b-b6e2-ff3eb810d47f","filename":"evidence_testing_on_gpt-5.6_high.jpg","category":"difficulty_self_test_evidence","content_type":"image/jpeg","size_bytes":148198},{"file_id":"1558ffd0-4864-446f-9754-85d8e5cb2dc3","filename":"run_report(9).txt","category":"difficulty_self_test_evidence","content_type":"text/plain","size_bytes":2239}],"deep_review":{"overall_verdict":"Accept","review_text":"**Overall Verdict:** Accept — The submission is complete, internally consistent, objectively evaluable, and end-to-end reachable, with minor evaluator-hardening and metadata-alignment improvements advisable.\n\n**Completeness Check:**\n- Instruction: Complete — The solver-facing documents clearly define the six categories, in-vivo scope, exact-span rules, workbook schema, confidence values, notes, and required offsets.\n- Inputs: Complete — The workbook contains 20 unique cases and 120 initialized span cells; its abstract text exactly matches the 20-record JSONL source.\n- Toolchain: Complete — LibreOffice Calc and Python 3 provide a free, standard, single-machine workflow; the evaluator uses only standard-library functionality.\n- Reference output: Complete — The CSV and reference workbook contain the same 433 unique spans across all 20 cases and six labels; every reference offset resolves to its annotated text, with no duplicate or overlapping reference spans.\n- Rubric: Complete — The 60% macro-F1 and 40% micro-F1 calculation is deterministic and bounded, although the evaluator implementation should strengthen enforcement of the stated structural gate.\n\n**File and Task Reachability:**\nAttachment classification: `reachable`. Task classification: `reachable`.\n\nAll manifest attachments were inspected. `SPAN_FORMAT_GUIDE.md` and `TASK_INSTRUCTIONS.md` are readable text; `pico_cases.jsonl` and `case_texts.jsonl` are valid, byte-identical 20-record JSONL files. `pico_span_extraction.xlsx`, `hidden_reference.xlsx`, and `pico_span_extraction_output.xlsx` are valid, unencrypted, single-sheet OOXML workbooks without macros, external relationships, unsafe archive entries, or excessive expansion. `hidden_entities.csv` is a readable 433-record reference table matching the reference workbook. `score_pico_submission.py` was inspected statically without execution. The self-test workbook has valid JSON, offsets, confidence values, notes, sorting, and non-overlap; independent reconstruction gives approximately 0.468 macro-F1, 0.538 micro-F1, and 0.496 weighted score, consistent with the result screenshot. Both JPEG evidence files were visually inspected, and `run_report(9).txt` is readable UTF-8 text. No attachment was left unreviewed and no material tooling limitation remained.\n\nThe completion path is direct: annotate the supplied abstracts in the template, preserve source text and case identifiers, validate JSON offsets locally, and return the completed workbook. Evaluation can parse that workbook, compare exact label/start/end tuples against the hidden reference, apply the weighted F1 rubric, and check structural completeness.\n\nLicense 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:**\n`public_benchmark` — The hidden references contain persistent public article identifiers and explicit provenance to a named annotated-corpus archive. The protected annotations may therefore be externally retrievable. Keep reference assets evaluator-only and enforce a technically offline solver environment; the submitted closed-book instruction alone is not a technical control.\n\n**Recommended Changes:**\nThe core task, data, references, and scoring design are valid; no contributor-side revision is required.\n\n- During evaluator integration, make missing, extra, or duplicate case IDs and other hard structural violations affect the numeric score. The supplied scorer currently records duplicate IDs only in `perfect_pass`, allowing an otherwise perfect duplicate-containing submission to retain a score of 1.\n- Harmonize the top-level output description with `TASK_INSTRUCTIONS.md`, which additionally requests `run_report.txt`.\n- Clarify the “<1%” self-test estimate: the supplied evidence supports zero exact-document matches but an overall weighted score of approximately 49.6%.","reviewed_at":"2026-08-15T02:40:04.064620Z"},"claim":{"id":"7b4a8097-030f-4761-aacf-df7f1c4af924","submission_id":"33ea1e9f-bdf5-4bce-a2cd-0e4021e95612","submission_revision_id":"fcb52bf2-8211-4c35-a12a-06acd5d6bf0e","builder":"builder-01","status":"active","claimed_at":"2026-10-15T14:58:24.384252Z","delivered_at":null,"ended_at":null,"delivery_note":null,"end_reason":null}},"revision_is_current":true}