{"submission_id":"fdfceb54-c51e-45e1-b3ac-6d887bb1dda6","submission_revision_id":"dd601934-d9e8-4151-810e-1bd8b6cba362","revision_version":1,"submission_status":"under_review","submitted_at":"2026-09-29T21:29:50.688058Z","submitter_name":"HAIXIANG TANG","submitter_email":"hansont2@illinois.edu","task":{"status":"submitted","version":1,"extra_fields":{},"submitted_at":"2026-09-29T21:29:50.688058+00:00","industry_domain":"Transportation and Urban Planning","task_short_name":"hsl_night_service_connection_audit_instance_1","operating_system":"Linux","task_description":"Audit scheduled night public-transport service at two Helsinki-region interchange areas using a frozen, authentic HSL GTFS extract. Reconstruct service dates and hub stop events for routes 15, 102N, 200, 202N, 227N, 510, A and L. Produce service_events.parquet, night_service.csv, connections.csv and audit.json following TASK.md, scope.json and contract.json in the input ZIP.\n\nExpand weekday calendars and dated exceptions over 11–19 October 2026. Evaluate seven nights, 12–18 October, each from 22:00 through but excluding 02:00 the next date. Correctly retain times greater than 24:00 on their source service dates, preserve separate stop-sequence visits, respect boarding/alighting restrictions, distinguish station groups from similarly named stops and retain the distinction between exact and estimated published timepoints.\n\nCalculate each hub/route/direction's departure count, distinct departure times, first/last departure offsets, maximum service gap including window boundaries, simultaneous-departure pairs and after-midnight count. For each eligible arrival and other target route/direction at the same hub, identify the earliest scheduled departure within an inclusive one-hour search horizon with the declared tie-break order. Search beyond 02:00 when needed. Keep explicit no-opportunity rows. A scheduled opportunity is not a feasible itinerary: the source provides no minimum transfer-time rule at these hubs, so physical feasibility must remain unknown. Do not invent walking times, actual reliability or compliance with a historical plan.\n\nAll required output schemas, null rules, status codes, row keys, scoring weights and hard-failure checks are visible in the inputs. Evaluation compares complete keyed tables and structured audit fields, not report prose. The work uses genuine published timetable records without synthetic anomalies. HSL's published transport-service planning provides professional context, but this is a newly authored, AI-assisted audit of a later snapshot, not a claim of participation in or exact reproduction of the original planning project.\n\nThe two-hour duration is a target, not a measured human completion time. Computation is offline after dependencies and inputs are supplied. No accounts, proprietary applications or paid data are needed. HSL source data are CC BY 4.0 with HSL/date attribution; authored task and evaluator materials carry a CC0 dedication. No HSL endorsement or independent human expert sign-off is claimed.","software_licensing":"Free/Open Source","evaluation_criteria":null,"verification_method":"The protected evaluator ZIP includes all four executed reference outputs, an executable output-only grader, a reproducible reference builder, a separate SQLite implementation and 56 passing output-mutation tests. Keep the evaluator ZIP unavailable to the evaluated agent before its attempt.\n\nThe grader assigns 30 points to service_events.parquet, 25 to night_service.csv, 30 to connections.csv and 15 to audit.json. Every schema, key, field, null rule, value domain and integrity gate is declared in solver-visible TASK.md and contract.json. Values are integer counts/seconds or categorical identifiers, so comparisons are exact; table row order and ID-list order are immaterial. Scores are keyed cell fractions with informative partial credit; full success requires every component correct. Missing/unreadable/empty required outputs, wrong schemas/types, duplicate keys, invalid dates, out-of-range values and declared internal inconsistencies yield zero.\n\nThe reference has 7,349 dated hub events, 105 night-service rows and 3,729 connection rows. Among connections, 1,749 have a schedule opportunity but unknown physical transfer feasibility, and 1,980 have no opportunity within the one-hour horizon. Thirty-two chosen departures are after the evaluated night's 02:00 endpoint but within the permitted search horizon. These reference counts belong to evaluation dependencies, not the solver instructions.\n\nRun python score.py --outputs path/to/candidate_outputs --grade grade.json from the extracted evaluator directory. Rebuild and test using evaluator README.md. The independent SQLite reconstruction compared all table cells and derived audit counts with the reference. Fresh ZIP extraction and reconstruction both score 100/100. Actual platform/timing/code/hash records are included. Local validation used Windows, Python 3.12.14 and PyArrow 19.0.1; Linux is the target, not a claimed tested platform. No human-review or autonomous-model difficulty self-test is claimed.","software_and_version":"CPython 3.12.14; PyArrow 19.0.1. Independent check: Python sqlite3 (SQLite 3.53.1 on the tested Windows machine). Linux target; local validation executed on Windows only.","specific_requirements":null,"expected_completion_time":null},"files":[{"file_id":"454726a3-418a-4dd3-a3f0-7e74f46fa412","filename":"solver_inputs.zip","category":"input_materials","content_type":"application/x-zip-compressed","size_bytes":2840555},{"file_id":"191948aa-3863-47a6-a6f4-71c95c9daebc","filename":"evaluator_bundle.zip","category":"reference_output","content_type":"application/x-zip-compressed","size_bytes":139520}],"deep_review":{"overall_verdict":"Strong Accept","review_text":"**Overall Verdict:** Strong Accept — The submission is complete, professionally scoped, reachable, and supported by reference outputs that match independent reconstruction from the supplied data.\n\n**Completeness Check:**\n\n- Instruction: Complete — Dates, hub membership, service-day arithmetic, restrictions, tie-breaking, null rules, and interpretation limits are explicit.\n- Inputs: Complete — The frozen GTFS extract, scope, contract, attribution, and provenance records are supplied; declared sizes, row counts, and packaged hashes match.\n- Toolchain: Complete — Python and PyArrow provide a practical, open-source, single-machine workflow without external services during computation.\n- Reference output: Complete — All four required artifacts are present and readable; every keyed table cell and audit field matched independent reconstruction.\n- Rubric: Complete — Visible requirements define exact comparisons, 30/25/30/15 weighting, partial credit, and hard failures. Grader inspection supports these rules, and supplied records report 56 passing tests.\n\n**File and Task Reachability:**\n\nAttachment classification: `reachable`. Task classification: `reachable`.\n\nBoth `solver_inputs.zip` and `evaluator_bundle.zip` were inspected, including every archive member. Neither archive presents encryption, unsafe paths, corruption, or unreasonable expansion.\n\nInspected materials include `TASK.md`, `scope.json`, `contract.json`, `SOURCES.md`, both package manifests, the source manifest, and all nine GTFS tables: agency, calendar, calendar exceptions, feed information, routes, stops, stop times, transfers, and trips. Evaluation materials inspected include the README, grader, reference builder, independent SQLite checker, mutation-test source, validation records, and all four reference outputs.\n\nNo required member remains uninspected. Uploaded programs were reviewed statically, not executed. The reported tests were not rerun. Linux execution and external provenance, licensing assertions, and possible public benchmark overlap were not independently verified; these limitations do not reveal a material workflow barrier.\n\nThe completion path is coherent: expand calendars, reconstruct dated hub events, calculate night metrics and bounded connection opportunities, write the four contracted outputs, and evaluate them against the protected reference. Solver inputs and evaluation answers are cleanly separated and must remain so.\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\nNone.\n\n**Recommended Changes:**\n\nThe task combines authentic schedule data, precise computational requirements, objective evaluation, and appropriately bounded transportation claims. No changes are required.","reviewed_at":"2026-10-14T21:08:16.625479Z"},"claim":null}