{"submission_id":"3af964cb-414e-410d-9792-0f94805fd539","submission_revision_id":"1f49d540-fe7a-4632-a615-c68fd1eccb12","revision_version":2,"submission_status":"under_review","submitted_at":"2026-08-16T07:39:20.744695Z","submitter_name":"Ziao Yang","submitter_email":"ziaoyang@brandeis.edu","task":{"status":"submitted","version":2,"extra_fields":{},"submitted_at":"2026-08-16T07:39:20.744695+00:00","industry_domain":"Data-Centric Machine Learning / AI Research","task_short_name":"calibrated_knn_shapley_audit_1","operating_system":"Linux","task_description":"You are the data valuation owner for a species-classification release built from real Palmer Archipelago penguin measurements. Read METHOD_SPEC.md, DATA_PROVENANCE.md, the four CSV tables, policy.json, and the pinned runtime manifest. Implement output/valuation_pipeline.py with the documented CLI and run it to create output/submission.json using Python 3.12.13 and NumPy 2.3.5. Compute weighted ordinary and calibrated KNN-Shapley values, audit naturally occurring value inflation and low-value records, execute source-cap/class-floor/total-cap curation, validate the curated KNN release, and process the 2009 archive intake sequentially. Labels and structural measurements are unchanged source observations. Do not hardcode visible ids: the evaluator replays the CLI on a disjoint hidden partition.","software_licensing":"Free/Open Source","evaluation_criteria":null,"verification_method":"The visible submission contributes 35% and an isolated replay of valuation_pipeline.py on a disjoint real-data partition contributes 65%. Each case hard-gates on the exact nested JSON schema, required scalar types and enums, complete unique ids, valid action-list partitions, and a complete disjoint curated/removed partition. Exact promised list order is scored. Weighted scoring covers ordinary/calibrated values (30%), detrimental/low-value/inflated sets and source audit (15%), source/class/total governed curation (25%), held-out predictions and cohort metrics (12%), and sequential intake decisions (18%). Both cases exercise total_cap, source_cap, and class_floor. References are absent during start; evaluation locks and snapshots them, deletes staging before replay, and runs the submitted pipeline as nobody. Authoring replay completes in under one second per case on the development machine; the task verifier budget is under one minute.","software_and_version":"Python 3.12.13, NumPy 2.3.5","specific_requirements":null,"expected_completion_time":null},"files":[{"file_id":"ee5e1b42-5757-48e8-9152-64acae0460e0","filename":"input_materials.zip","category":"input_materials","content_type":"application/zip","size_bytes":11960},{"file_id":"c540ad8d-293c-402e-b698-781714ddc6b9","filename":"reference_output_and_evaluation_dependencies.zip","category":"reference_output","content_type":"application/zip","size_bytes":52690}],"deep_review":{"overall_verdict":"Strong Accept","review_text":"**Overall Verdict:** Strong Accept — The submission is complete, coherent, securely evaluated, professionally scoped, and fully reachable without material defects.\n\n**Completeness Check:**\n- Instruction: Complete — `METHOD_SPEC.md` defines the formulas, weighting, tie-breaking, ordered governance rules, CLI, rounding, and exact nested output schema.\n- Inputs: Complete — Both cases contain readable, internally consistent training, validation, held-out, stream, policy, and provenance files with complete unique IDs, valid schemas, no missing fields, and disjoint source-row partitions.\n- Toolchain: Complete — CPython 3.12.13 and NumPy 2.3.5 are pinned consistently through the Python version file, project manifest, requirements, and lockfile; the CPU-only workload is small and uses freely obtainable software.\n- Reference output: Complete — Schema-valid visible and hidden gold outputs, a matching expert submission, reusable expert pipeline, and checksum-pinned reference manifest provide a complete expected-artifact contract.\n- Rubric: Complete — The scorer hard-gates schema, scalar types, enums, ID completeness, and partitions, then applies the documented component weights and 35% visible/65% isolated hidden replay.\n\n**File and Task Reachability:**\nThe attachment classification is `reachable`, and the end-to-end task classification is `reachable`. Both ZIP containers matched their declared sizes, parsed without errors, contained only regular unencrypted members, had safe paths and modest expansion, and every extracted member matched its authoritative attachment-manifest SHA-256.\n\nAll attachment members were inspected: both `ATTACHMENT_MANIFEST.json` files; `METHOD_SPEC.md`; visible and hidden `DATA_PROVENANCE.md`, CSV tables, and `policy.json`; `.python-version`, runtime `README.md`, `pyproject.toml`, `requirements.txt`, and `uv.lock`; `README_FOR_ALE.md`; evaluator `README.md`, `PROVENANCE.md`, `ORIGINALITY.md`, `main.py`, `score_outputs.py`, and `task_card.json`; `REFERENCE_RUN_MANIFEST.json`; both gold submissions; and the expert pipeline and submission. No attachment content was left unreviewed. Submitted Python was inspected statically and was not executed. External provenance and license URLs were not followed, so those public facts were not independently network-verified.\n\nThe visible case has 54 training, 37 validation, 20 held-out, and 59 stream records; the hidden case has 55, 37, 20, and 60. Reference invariants confirm ordered lists, disjoint complete curation partitions, stream action partitions, source/class/total-cap branches, and all intake actions. The evaluator statically demonstrates reference absence during solving, root-owned bounded snapshots, deletion before replay, unprivileged execution with resource limits, and weighted scoring against in-memory references.\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 underlying Palmer penguin dataset and cited KNN-Shapley method are publicly identifiable. Risk is materially mitigated because the task-specific governance workflow and outputs are bespoke, IDs differ by case, solver-visible inputs exclude references, and 65% of scoring comes from an isolated hidden replay rather than a canonical public answer.\n\n**Recommended Changes:**\nThe submission provides unusually strong specification, provenance, branch coverage, reference isolation, and objective scoring. No changes are required.","reviewed_at":"2026-08-16T07:47:35.839158Z"},"claim":null}