{"claim":{"id":"fbad8b6d-0a61-4382-affe-6539171544de","submission_id":"9da2998d-ad4a-4986-8384-ab32173f6744","submission_revision_id":"d064d154-ef35-45f0-bc60-a46ad83678d7","builder":"builder-01","status":"active","claimed_at":"2026-10-15T14:43:33.823387Z","delivered_at":null,"ended_at":null,"delivery_note":null,"end_reason":null},"task":{"submission_id":"9da2998d-ad4a-4986-8384-ab32173f6744","submission_revision_id":"d064d154-ef35-45f0-bc60-a46ad83678d7","revision_version":1,"submission_status":"under_review","submitted_at":"2026-07-30T18:54:35.315379Z","submitter_name":"Shengrui Lyu","submitter_email":"lsr@anthropic.com","task":{"status":"submitted","version":1,"extra_fields":{"difficulty_self_test":{"model":"Claude Opus 4.8","harness":"Claude code","estimated_score":"40–60%"}},"submitted_at":"2026-07-30T18:54:35.315379+00:00","industry_domain":"Energy / Geoscience (Petrophysics and Well-Log Data Management)","task_short_name":"well_log_digitizing_1","operating_system":"Linux","task_description":"Objective: Digitize a scanned paper wireline logging job into a calibrated LAS 2.0 file — the industry-standard \"raster-to-vector\" service that log-data companies perform on legacy field prints. The agent must recover seven named curves as continuous digital data from two scanned 1989 field prints, correctly separating overlapping traces and reconstructing the off-scale (backed-up) segments where pens leave the track and wrap.\n\nStarting State: The agent (a well-log data technician on a headless Linux machine, shell + Python, no GUI, no network) receives `input/log_scan/zdensilog_neutron_log.png` and `input/log_scan/dual_laterolog_log.png` — bilevel ~200 dpi scans of two paper wireline logs from ONE open-hole logging job run in 1989 on an onshore Netherlands gas well: a Compensated Z-Densilog / Compensated Neutron print (carries no gamma ray) and a Dual Laterolog / Micro Laterolog / Gamma Ray / SP print over the same interval (which presents the interval twice at 1:200, as \"GRONINGEN MODE\" and \"NORMAL MODE\" main logs). The well-identity part of each header and the company / well-name lines above every pass are redacted; scale headers, depth track, remarks, calibration pages and all traces are intact. Also provided: `input/task_brief.md` and `input/curve_list.md`. No network access; the well must not be identified and no catalogue data fetched.\n\nInstructions & Rules:\n1. Deliver `output/well_digitized.las` (LAS 2.0, unwrapped, parseable by the `lasio` Python package): depth index DEPT in metres, top 985.0 m, base 1195.0 m, step 0.15 m, null value -999.25, carrying exactly the seven curves listed in `curve_list.md` — ZDEN, CNC, PE digitized from the Z-Densilog print's 1:200 main log, and GR, RD, RS, RMLL digitized from the Dual Laterolog print's NORMAL MODE 1:200 main log — with the mnemonics and units given there.\n2. The only calibration source is the raster itself: read every track's scale ranges, line-style assignments and the depth grid off the scan (do not assume default scales).\n3. Correctly attribute the overlapping traces on the shared porosity track (density solid vs. neutron long-dash vs. PE short-dash, tangled with distractor traces) and separate the three laterolog resistivities on their logarithmic track.\n4. Reconstruct off-scale (backed-up) segments — density and neutron pens leaving the track and wrapping, gamma-ray spikes above the printed maximum, laterolog values pegged beyond the log grid — as continuous physical values, not clipped edges.\n5. Work entirely offline; a deliverable that reproduces catalogue data rather than a reading of these prints is not a solution.\n\nDATA ATTRIBUTION (required by the source's reuse conditions): The scans and the operator's digital run data are taken from NLOG.NL, the Dutch subsurface data portal operated by TNO — Geological Survey of the Netherlands, from a released (non-confidential) 1989 borehole. NLOG's terms of use grant reuse (copy, download, distribute, process and edit) on the condition that NLOG.NL is quoted as the source. Any redistribution must therefore carry the credit line: \"Source: NLOG.NL / TNO — Geological Survey of the Netherlands.\" The verbatim reuse grant with its URL is in the bundle's DATA_PROVENANCE.md and LICENSES.md.","software_licensing":"Free/Open Source","evaluation_criteria":null,"verification_method":"Deterministic, offline grader (Python; grade_las.py supplied in the reference bundle, ~2 s runtime, byte-for-byte deterministic). The hidden reference is the operator's own digital run data for this job (the acquisition LAS/LIS), unwrapped and resampled to the delivered grid; it was verified quantitatively that the scanned raster and the digital data register in depth to within the grader's shift tolerance.\n\nHard gates (score 0.0): output missing; file not parseable by lasio; no DEPT index; fewer than 20 valid points on a curve (that curve scores 0); a curve delivered under one name that matches a DIFFERENT reference curve better than its own (swap detector: margin 0.10, r >= 0.90) is scored as mislabelled = 0.\n\nScoring:\n- SCORE = sum_i(w_i * s_i) / sum_i(w_i), curve weights summing to 10: CNC 2.25, ZDEN 1.75, PE 1.75, GR 1.5, RS 1.0, RMLL 1.0, RD 0.75 (weights follow the phenomena that are objectively human-hard on the print - the porosity pile-up carries 5.75/10).\n- Per curve, after a +/-1 m depth-shift search and a calibration fit cand = a*ref + b (a in [0.95,1.05], |b| <= 3 percent of span; log10 space for the three laterologs): s_i = coverage * sqrt(accuracy * shape), where accuracy ramps from nrmse <= 1 percent (1.0) to >= 15 percent (0.0) and shape ramps from Pearson r >= 0.95 (1.0) to <= 0.60 (0.0).\n- Hard-zone weighting: reference-derived depth zones where a human digitizer objectively works slowest (backed-up density/neutron, gamma-ray wraps, resistivity peg-outs, porosity-track pen crossings) weight each sample 6x in coverage, RMS error and correlation, so roughly two thirds of the total score is decided in the hard zones.\n\nCalibration / fixtures (14 shipped, each with report.json; a 55-test pytest suite pins the values, hard gates, determinism, report schema and the swap detector): the operator reference resubmitted 1.000; a careful semi-automatic digitization with realistic noise 0.956; density off-scale clipped 0.984 and gamma-ray not-unwrapped 0.958 (this shallow job has few, shallow excursions, so mild clipping is only lightly penalized - the ordering is pinned by test); neutron backups not unwrapped 0.775; PE following the neutron trace 0.825; RS/RMLL swapped 0.800; the resistivity trio scrambled 0.725; density and neutron swapped 0.600; flat-line-all / depth-only / empty 0.000.\n\nDifficulty evidence (score reports shipped; solver notes and outputs excluded): three independent solver runs by a frontier-class model agent scored 0.410, 0.437 and 0.504; a synthetic-well gaming attempt (trace one real curve, fabricate the rest as physically self-consistent functions of it, correct format/coverage/ranges) scored 0.000. In all three solver runs the neutron porosity CNC scored 0.00 - the reproduced human-hard core; expert reference 1.0. Model-tier caveat as in the bundle README: treat 0.41-0.50 as an upper bound on ease; no target-tier pass rate is claimed.\n\nATTRIBUTION REMINDER (reuse condition): the reference and input data derive from NLOG.NL (TNO - Geological Survey of the Netherlands). The required credit line \"Source: NLOG.NL / TNO - Geological Survey of the Netherlands\" is reproduced in the bundle's DATA_PROVENANCE.md and LICENSES.md alongside the verbatim reuse grant and its URL, and must accompany any redistribution of the input or reference files.","software_and_version":"Python 3.11 with numpy, scipy, scikit-image, pillow, lasio (all open source); any raster/image tooling","specific_requirements":null,"expected_completion_time":null},"files":[{"file_id":"dd31ecc9-6a28-4a01-8acf-f0d4cd15d2dc","filename":"well_log_digitizing_input_materials.zip","category":"input_materials","content_type":"application/zip","size_bytes":2302877},{"file_id":"4109fcb4-881b-4e00-9482-2dc20057f1ab","filename":"well_log_digitizing_reference_and_evaluation.zip","category":"reference_output","content_type":"application/zip","size_bytes":3029863},{"file_id":"a5a1ea7d-03d3-4087-bd71-f375ef579815","filename":"well_log_digitizing_difficulty_evidence.zip","category":"difficulty_self_test_evidence","content_type":"application/zip","size_bytes":5223}],"deep_review":{"overall_verdict":"Accept","review_text":"**Overall Verdict:** Accept — The submission is complete, professionally representative, end-to-end reachable, and objectively evaluable; only minor evaluator/documentation alignment issues remain.\n\n**Completeness Check:**\n- Instruction: Complete — The required curves, source passes, calibration rules, depth grid, output format, offline constraint, and attribution obligation are explicit.\n- Inputs: Complete — Both readable 1-bit log scans and the supporting task and curve documents are present, consistent with their described roles, and visibly contain the required passes, grids, headers, traces, and redactions.\n- Toolchain: Complete — The declared Python 3.11 and open-source scientific imaging/LAS stack is legally obtainable and practical on the specified single-machine CPU environment.\n- Reference output: Complete — The evaluator bundle includes the operator-derived LAS reference, source LAS/LIS records, original TIFF scans, reference manifest, hard-zone definitions, provenance, fixtures, and reproducibility documentation, separated from solver-visible inputs.\n- Rubric: Unclear — The scoring implementation, configuration, reports, and tests are extensive, but the grader does not explicitly enforce the documented `DEPT` mnemonic hard gate, and the submission text lists the neutron-clipping fixture as 0.775 while its report, tests, and evaluator documentation consistently give approximately 0.844.\n\n**File and Task Reachability:**\nAttachment classification: `reachable`. Task classification: `reachable`. All three manifest attachments were downloaded at their declared sizes, identified as valid unencrypted ZIP archives, checked for integrity, traversal paths, links, and expansion risk, and inventoried in full. The inspected evidence included `well_log_digitizing_input_materials.zip`, `well_log_digitizing_reference_and_evaluation.zip`, and `well_log_digitizing_difficulty_evidence.zip`; within them, the task brief, curve list, both PNG scans, reference and source LAS files, LIS records, TIFF sources, manifest and hard-zone JSON, grader source, evaluation and provenance documents, environment specification, tests, all 14 LAS fixtures and paired reports, and all four difficulty reports were reviewed. The scans were also visually inspected. No relevant archive content was omitted. Uploaded programs were reviewed statically and not executed. The binary LIS files had recognizable record structure but were not semantically decoded with a specialized LIS parser; the accompanying LAS conversions and documented channel mappings provide the usable evaluation path. The solver can trace and calibrate the seven curves from the redacted scans, write the required LAS 2.0 output, and be evaluated offline against the hidden operator-derived reference with deterministic per-curve scoring. 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:**\n- `public_benchmark` — The source scans and protected digital truth originate from a publicly downloadable national subsurface portal. The risk of direct retrieval is materially mitigated by redacting the well identity and requiring a network-disabled solver environment. Whether the specific records have meaningful pretraining exposure could not be independently established without public search.\n\n**Recommended Changes:**\nThe submission provides unusually strong provenance, solver/reference separation, negative fixtures, hard-zone weighting, swap detection, and difficulty evidence. No material redesign is required. Align the advertised neutron-clipping fixture score with the shipped 0.8435 result, and either enforce an explicit `DEPT` index mnemonic in the grader with a regression test or revise the stated hard gate to match the implemented index behavior.","reviewed_at":"2026-08-13T23:09:30.967070Z"},"claim":{"id":"fbad8b6d-0a61-4382-affe-6539171544de","submission_id":"9da2998d-ad4a-4986-8384-ab32173f6744","submission_revision_id":"d064d154-ef35-45f0-bc60-a46ad83678d7","builder":"builder-01","status":"active","claimed_at":"2026-10-15T14:43:33.823387Z","delivered_at":null,"ended_at":null,"delivery_note":null,"end_reason":null}},"revision_is_current":true}