Release attributed source audit and all frozen causal decision and episode traces

ACCESS-LEDGER.md

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+# Data access and confirmation boundary
+
+Recorded **2026-10-04**. The complete source audit occurred **before** the model
+protocol was frozen. Its declared metadata access is part of the study, rather
+than a claim that source files were never read.
+
+| Phase | Data processed | Information used for research choices | Performance access |
+| --- | --- | --- | --- |
+| Prospective preflight | Twelve predeclared files, 954,647 rows; five real filename groups | Source format, units, label gaps, availability and checkpoint compatibility | No fitting/scoring; whole five groups quarantined to training |
+| Complete source qualification | All 2,228 recordings / 76,587,318 rows in the private CPU audit | Hashes, schemas, clocks, label counts/runs/support, numeric validity/missingness and exact duplicate fingerprints | No feature distributions, fitted predictions, alarms or performance scores exported |
+| Prospective study freeze | Qualified metadata for all 40 real filename groups | Fixed group strata, source-supported denominators, sensor panel and deterministic nonoverlapping recording selection | No validation/confirmation features, fitted predictions or score distributions used |
+| Initial model fit/selection | Selected training payloads, followed by validation payloads | Training-only scaling/weights; bounded fixed candidates; validation-only fixed alarm budgets/thresholds and primary selection | Initial confirmation payloads not decoded for features until the selection receipt is written |
+| Initial confirmation/reload | Eight initial confirmation groups; safe reload probes exported afterward | Frozen scoring and artifact verification | Results retained without retuning; no new candidate or threshold selection |
+| Later foundation study | Eight additional reserved groups | A separate task/source/pipeline protocol must be reviewed and frozen first | Reserved feature/prediction/performance data are not accessed by the initial fitting job |
+
+Source qualification necessarily decoded numeric values to count unusable
+observations and hash normalized sensors. It also examined label-support metadata
+for every group. Sensor availability and source support informed design; this
+is not a claim of an entirely metadata-blind split. The qualifying code did not
+export numerical value distributions or optimize a model from held-out features.
+
+Similarly, the model job hashes the **whole ZIP** before fitting. That reads
+compressed bytes corresponding to reserved recordings. It does not extract those
+recordings for model features, inference or tuning. Here, “reserved/unopened”
+means **unopened by fitting, selection and performance evaluation**; it does not
+mean their source bytes were never hashed or their support metadata never audited.
+The freeze's value-access declaration uses this predictive-study boundary.
+
+The source metadata, protocol, manifest and original source-freeze receipts are
+retained unchanged. Initial confirmation outcomes must not become tuning data
+under the same fresh-confirmation claim. A public pretrained model's exposure to
+3W is a separate unresolved contamination question; reserved study groups do not
+prove they were absent from public pretraining.

ATTRIBUTION.md

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+# Attribution and licences
+
+Petrobras 3W 2.0 is the source of public production-event measurements/labels.
+Source: https://github.com/petrobras/3W/tree/6a13bd21a02a2ce6ce23a02b1c2ba2f73770752b
+Data descriptor: https://doi.org/10.1038/s41597-026-07225-z
+Versioned archive: https://doi.org/10.6084/m9.figshare.29205836.v1
+Data licence: CC BY 4.0, https://creativecommons.org/licenses/by/4.0/
+Upstream source/toolkit code: Apache 2.0. Third-party code and paper figures are
+not bundled in this release.
+
+Sankalp Thakur performed source qualification, deterministic group/interval
+selection, causal feature extraction, training, score/alarm replay and derived
+metadata/decision/episode exports. These are adaptations and research results,
+not Petrobras endorsement, a replication of ADIPEC field results or new field
+measurements. Original anonymized filename groups/timestamps are already public.
+
+Authored code and trained numeric weights: MIT (see LICENSE in the model repo).
+3W-derived tables, scalers, feature/reload probes and traces: CC BY 4.0. Preserve
+the upstream attribution and describe further changes when redistributing them.
+No original client architecture, private plant tags or access credentials are
+included. No model admission, plant access, control or safety-system credit.

PUBLICATION-MANIFEST.json

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+      "sha256": "7baeeb57337fa790acf4d838930c5c1b6959b821594686403dfcfe953b91e283"
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+      "sha256": "6048287bf1df25894566a174aa862688a5cb0eb25dfdbf4a7f8041784c5c1716"
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+      "bytes": 5040,
+      "sha256": "001d22bf52f576287f20663a319163bb7a69376aed288c2bef620daf5b7a4de6"
+    }
+  },
+  "private_client_architecture_included": false,
+  "model_admission": false,
+  "control_permission": false
+}

README.md

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+---
+license: cc-by-4.0
+task_categories: [tabular-classification]
+tags: [time-series, industrial, 3w, causal, negative-results, anomaly-detection]
+configs:
+- config_name: decisions
+  default: true
+  data_files:
+  - split: validation
+    path: data/decisions/validation.parquet
+  - split: confirmation
+    path: data/decisions/confirmation.parquet
+- config_name: episodes
+  data_files:
+  - split: validation
+    path: data/episodes/validation.parquet
+  - split: confirmation
+    path: data/episodes/confirmation.parquet
+- config_name: corpus
+  data_files:
+  - split: metadata
+    path: data/corpus/metadata.parquet
+---
+# 3W causal real-group audit and failed-transfer traces
+
+Native typed Parquet derived from pinned public Petrobras 3W 2.0. This release
+contains source-audit metadata and every validation/confirmation decision and
+episode outcome for the frozen 11-candidate study. **It is not a new raw sensor
+corpus, new field acquisition or industrially qualified failure dataset.**
+
+The [source descriptor](https://doi.org/10.1038/s41597-026-07225-z) is published
+in April 2026; the underlying pinned release predates that paper. See
+[attribution](ATTRIBUTION.md), [exact table schemas/counts](TABLES.json),
+[protocol](benchmark-protocol.json), [manifest](benchmark-manifest.json),
+[access ledger](ACCESS-LEDGER.md), and [complete negative-result report](https://huggingface.co/sankalpsthakur/well-production-event-causal/blob/main/well_health/REPORT.md).
+The model repository is [well-production-event-causal](https://huggingface.co/sankalpsthakur/well-production-event-causal).
+
+## Configurations
+
+- `decisions`: one row per 30-second decision after 180-second past-only warmup;
+  labels, numeric eligibility, capped grid exposure, all 11 class/residual scores
+  and frozen alarms. Unknown class/binary targets and ineligible scores are null.
+  `event_timestamp_ns` is the original nominal dataset event clock, not verified
+  arrival or decision time. Scores/labels/provenance are offline audit fields;
+  they must not enter a predictor as input features.
+- `episodes`: one row per candidate/episode, with event, onset censoring, alarm
+  coverage, carried-in alarm and known-onset delay. Transient and steady labels
+  for the same contiguous event are merged. Covered is not onset detected.
+- `corpus`: 2,228 source recording metadata rows, including 1,119 real,
+  1,089 simulated and 20 drawn files, source hashes, labels, clocks, seven-channel
+  availability and the frozen real-recording selection/overlap exclusions.
+  Reserved-group rows are qualification metadata, not predictive evaluations.
+
+No train or reserved feature/score split is published here. The protocol used
+357 training, 33 validation, 138 initial confirmation and 113 reserved selected
+recordings, plus 478 real overlap exclusions. Raw sensor values are obtained
+from the separately attributed upstream source, not silently duplicated here.
+Filename groups are anonymized public IDs, not authenticated physical-well IDs.
+
+## Known failure and intended use
+
+The validation-selected primary covers 24/55 confirmation episodes, with 42
+false alarms over 9.124 eligible normal days and 64.392% normal alarm occupation.
+Only 3/12 observable onsets are covered within 300 seconds; most covered episodes
+already had an active alarm at onset. Normal numeric eligibility is 59.812%.
+Use these tables to inspect group transfer, abstention, onset censoring and alarm
+burden, not to claim physical barrier integrity, RUL or deployment readiness.
+
+All 31 frozen fixtures passed in the actual included CPU run. Its final JSON
+report failed serialization after traces/weights were saved; a separate report
+was recovered without fitting/retuning. The provider failure remains explicit.
+The original confirmation set is consumed; eight additional groups stay reserved
+for a separately reviewed/frozen future comparison. Public-pretraining exposure
+cannot be excluded by study-level group separation.

TABLES.json

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+{
+  "decisions/validation": {
+    "rows": 140543,
+    "columns": 31,
+    "schema": [
+      {
+        "name": "recording",
+        "type": "string"
+      },
+      {
+        "name": "filename_group",
+        "type": "string"
+      },
+      {
+        "name": "source_sha256",
+        "type": "string"
+      },
+      {
+        "name": "decision_row",
+        "type": "int64"
+      },
+      {
+        "name": "event_timestamp_ns",
+        "type": "int64"
+      },
+      {
+        "name": "class_label",
+        "type": "int32"
+      },
+      {
+        "name": "binary_target",
+        "type": "int8"
+      },
+      {
+        "name": "numeric_input_eligible",
+        "type": "bool"
+      },
+      {
+        "name": "grid_exposure_seconds",
+        "type": "int32"
+      },
+      {
+        "name": "score__signal_logistic_C0.1",
+        "type": "float"
+      },
+      {
+        "name": "alarm__signal_logistic_C0.1",
+        "type": "bool"
+      },
+      {
+        "name": "score__signal_logistic_C1.0",
+        "type": "float"
+      },
+      {
+        "name": "alarm__signal_logistic_C1.0",
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+      },
+      {
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+      },
+      {
+        "name": "alarm__signal_logistic_C10.0",
+        "type": "bool"
+      },
+      {
+        "name": "score__missingness_logistic_C0.1",
+        "type": "float"
+      },
+      {
+        "name": "alarm__missingness_logistic_C0.1",
+        "type": "bool"
+      },
+      {
+        "name": "score__missingness_logistic_C1.0",
+        "type": "float"
+      },
+      {
+        "name": "alarm__missingness_logistic_C1.0",
+        "type": "bool"
+      },
+      {
+        "name": "score__missingness_logistic_C10.0",
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+      },
+      {
+        "name": "alarm__missingness_logistic_C10.0",
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+      {
+        "name": "score__causal_tcn_seed7",
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+      {
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+    "sha256": "6199b144aa5890350efe32b7294ada9219f551fe721f9a4e10b27490dd58f56a"
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+    "columns": 12,
+    "schema": [
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+        "type": "string"
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+        "type": "bool"
+      },
+      {
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+        "type": "int32"
+      },
+      {
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+        "type": "bool"
+      },
+      {
+        "name": "carried_in_alarm",
+        "type": "bool"
+      },
+      {
+        "name": "first_covered_row",
+        "type": "int64"
+      },
+      {
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+        "type": "int64"
+      }
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+        "type": "string"
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+        "type": "string"
+      },
+      {
+        "name": "source_sha256",
+        "type": "string"
+      },
+      {
+        "name": "decision_row",
+        "type": "int64"
+      },
+      {
+        "name": "event_timestamp_ns",
+        "type": "int64"
+      },
+      {
+        "name": "class_label",
+        "type": "int32"
+      },
+      {
+        "name": "binary_target",
+        "type": "int8"
+      },
+      {
+        "name": "numeric_input_eligible",
+        "type": "bool"
+      },
+      {
+        "name": "grid_exposure_seconds",
+        "type": "int32"
+      },
+      {
+        "name": "score__signal_logistic_C0.1",
+        "type": "float"
+      },
+      {
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+      },
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+      },
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+        "type": "string"
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+        "type": "string"
+      },
+      {
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+        "type": "string"
+      },
+      {
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+        "type": "int32"
+      },
+      {
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+        "type": "int64"
+      },
+      {
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+        "type": "int64"
+      },
+      {
+        "name": "known_onset",
+        "type": "bool"
+      },
+      {
+        "name": "eligible_decisions",
+        "type": "int32"
+      },
+      {
+        "name": "alarm_covered",
+        "type": "bool"
+      },
+      {
+        "name": "carried_in_alarm",
+        "type": "bool"
+      },
+      {
+        "name": "first_covered_row",
+        "type": "int64"
+      },
+      {
+        "name": "known_onset_delay_seconds",
+        "type": "int64"
+      }
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+        "type": "string"
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+        "type": "string"
+      },
+      {
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+        "type": "string"
+      },
+      {
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+        "type": "int64"
+      },
+      {
+        "name": "source_bytes",
+        "type": "int64"
+      },
+      {
+        "name": "first_timestamp_ns",
+        "type": "int64"
+      },
+      {
+        "name": "last_timestamp_ns",
+        "type": "int64"
+      },
+      {
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+        "type": "int64"
+      },
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+        "type": "int64"
+      },
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+    ],
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+    "sha256": "7baeeb57337fa790acf4d838930c5c1b6959b821594686403dfcfe953b91e283"
+  }
+}

benchmark-protocol.json

@@ -0,0 +1,47 @@
+{
+  "schema": "industrial.well-health.benchmark.protocol.v1",
+  "reviewed_on": "2026-10-04",
+  "task": "Retrospective causal binary undesirable-event detection, including labelled transients; not a pre-onset forecast or physical well-integrity diagnosis.",
+  "source_revision": "6a13bd21a02a2ce6ce23a02b1c2ba2f73770752b",
+  "corpus_audit_sha256": "de221eed3ddc1a1a09d7d8c31be0a4758afd06bf03bd20baa67de23144941717",
+  "archive_sha256": "c037a6a9d5dcc9b2add7b9ea413f5cff367c57bad3a93faac1d1d32da27fcbea",
+  "metadata_access": "Complete schema, label support/runs, missingness, clock spans and duplicate metadata were inspected before this freeze. Only the 12 prospective development samples had local sensor-value access. No confirmation values, fitted scores or distribution summaries were inspected.",
+  "split_seed": "3w-causal-well-groups-20261004-v1",
+  "group_split": "Quarantined groups go to training. Other groups stratified into normal-only or positive-supported, sorted by SHA256(seed + ':' + group). First ceil(20%) reserved fresh foundation confirmation, next ceil(20%) initial confirmation, next ceil(20%) validation, remainder training. No class tailoring or value-based split selection.",
+  "quarantine": ["WELL-00001", "WELL-00002", "WELL-00010", "WELL-00015", "WELL-00019"],
+  "record_overlap": "Within each real group, earliest-finish interval scheduling with start/path tie breaks; accept a complete recording only when its start is strictly later than the last accepted end. Retain every excluded path/reason. Do not stitch files. Intervals include unknown prefixes.",
+  "origins": "Real recordings only in this initial study; simulated/drawn data are not silently added to training or confirmation.",
+  "channels": ["P-ANULAR", "P-JUS-CKGL", "P-MON-CKP", "P-PDG", "P-TPT", "QGL", "T-TPT"],
+  "units": {"P-ANULAR": "Pa", "P-JUS-CKGL": "Pa", "P-MON-CKP": "Pa", "P-PDG": "Pa", "P-TPT": "Pa", "QGL": "m3/s", "T-TPT": "degC"},
+  "unit_boundary": "Source units are used as declared. Absolute versus gauge pressure, physical calibration, delivery latency and clocks are not independently verified.",
+  "forbidden_predictive_fields": ["class", "state", "well_id", "origin", "event_directory", "recording_name"],
+  "sample_period_seconds": 1,
+  "window_seconds": 180,
+  "decision_stride_seconds": 30,
+  "bin_seconds": 15,
+  "eligibility": "At least two of the five pressure channels must have >=90% finite numeric observations in the past 180 seconds. No future filling, cross-record history, or treatment of unknown labels as normal.",
+  "arrival_assumption": "Retrospective replay assumes nominal 1Hz rows arrive by each decision. Dataset timestamps alone cannot establish live arrival/quality. Finite numeric values are not verified physical sensor quality.",
+  "features": "For each channel: current value, past mean, past standard deviation, last-minus-first available value and finite fraction. Twelve consecutive past 15-second means and finite fractions form the TCN input. Unavailable values are filled with training-only channel means after normalization, encoded as zero with explicit masks. No label/time/identity features.",
+  "scaling": "Fit channel mean/std and summary feature mean/std on at most 30000 eligible known-label training windows selected by seeded RNG. Do not fit on validation or confirmation. Clamp normalized values to +/-10; unchanged training scalers used for all groups.",
+  "training_windows_maximum": 30000,
+  "training_seed": 407,
+  "training_weights": "Equal aggregate weight per (training group, binary class) cell present. Both classes must exist; no oversampling from validation/confirmation.",
+  "logistic_C": [0.1, 1.0, 10.0],
+  "logistic_max_iter": 400,
+  "missingness_baseline": "Logistic models using only seven finite fractions, with the same C search, training windows and group/class weights.",
+  "conventional_references": ["Maximum absolute normalized past-window channel mean", "Nonnegative one-sided cumulative sum of mean absolute normalized channel means minus k, clipped at 100; k=0.5 and k=1.0. State resets on ineligible input or nonconsecutive decisions."],
+  "reference_scaling": "For conventional references only, fit per-channel median and 1.4826*MAD of eligible known-normal training-window means, with scale floor 1e-6; clamp absolute residuals at 10. No online label access or per-test-well adaptation.",
+  "tcn": {"width": 16, "kernel_size": 3, "dilations": [1, 2, 4], "seeds": [7, 29], "epochs": 8, "batch_size": 256, "learning_rate": 0.001, "weight_decay": 0.0001, "maximum_parameters": 100000, "checkpoint": "Final fixed epoch; no validation-driven epoch selection or architecture changes."},
+  "candidate_count": 11,
+  "alarm": {"on_consecutive_decisions": 2, "off_consecutive_decisions": 2, "reset_on_ineligible": true, "threshold_candidates": "49 quantiles from 0.90 through 1.00 of eligible known-normal validation scores, plus null as an explicitly disabled threshold. All candidates and thresholds retained.", "validation_false_alarms_per_eligible_normal_day_max": 1.0, "validation_normal_alarm_occupation_max": 0.01, "minimum_eligible_validation_normal_days": 0.5},
+  "selection": "For each candidate, among thresholds meeting both budgets select maximum all-episode alarm coverage, then known-onset coverage within 300s, then lower false-alarm rate, lower normal alarm occupation and higher threshold. Insufficient eligible normal time selects disabled threshold and records the failure. Global primary candidate uses that performance ordering without comparing numeric thresholds across model scales, then fewer model parameters, then lexical candidate name. Thresholds and primary candidate are written before opening confirmation payloads.",
+  "confirmation": "Open once after the selection receipt is written; evaluate every predeclared candidate at its frozen validation threshold. The validation-selected primary remains primary even when another candidate has better confirmation results. No retuning after confirmation.",
+  "metrics": ["All-episode alarm coverage including censored episodes", "Known-normal-to-positive onset coverage within 300 seconds and detection delay", "Per-event and group support", "False normal alarm episodes per eligible known-normal decision-grid day", "Normal alarm occupation", "Known-normal/positive input eligibility and unknown-label duration", "Brier score only for probabilistic candidates", "95% group bootstrap intervals, 1000 resamples with seed 611; zero-denominator replicates excluded and counted"],
+  "metric_boundary": "Decision-grid exposure is stride-weighted, capped at recording end; exact raw class durations are reported separately. An alarm already active when an event begins is reported as carried-in coverage. Censored onsets cannot establish delay, and event labels do not establish actionable lead time or barrier failure.",
+  "artifact_reload": "Safe numeric arrays/JSON; no arbitrary pickle loading. After frozen confirmation finishes, retain 128 seeded eligible confirmation summary/temporal inputs and corresponding scores for independent checkpoint replay, and all conventional-reference feature inputs for stateful replay. Cross-host probabilistic score absolute tolerance 1e-5; same-host weights require exact numeric reload. No fitting or retuning during replay.",
+  "resources": "Private included Kaggle CPU; two software threads; at most 7200 seconds. Internet disabled during fitting. Full raw timings and process lifetime RSS retained; these do not establish PLC scan deadlines.",
+  "model_admission": false,
+  "control_permission": false,
+  "plant_access": false,
+  "foundation_comparator": "Not included in v1 fitting. Reserved groups remain unopened by this job. TSPulse requires a separate reviewed task/pipeline protocol before using that fresh confirmation; no pretrained-model result is fabricated."
+}

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