Pull Testing and Statistical Evaluation of ILI Capabilities on SSWC and Other Anomalies
Bibliographic record
Abstract
Abstract TC Energy (TCE) operates and maintains natural gas, refined products, and crude oil pipelines in the USA, Canada, and Mexico. The network includes more than 93,000 kilometers (58,000 miles) of gas pipelines, transporting more than 30% of the North American natural gas demand. As part of its Integrity Management Program, TCE identified Selective Seam Weld Corrosion (SSWC) as one of the significant threats for certain seam weld types in their pipeline system. It also identified In-Line Inspection as the preferred methodology to address it. In that regard, multiple ILI vendors have claimed capabilities in detecting, identifying, and sizing SSWC features, which require careful evaluation and assessment. A pull-through testing exercise was designed to evaluate and validate those capabilities statistically. The evaluation aims to test multiple ILI systems from different ILI vendors for SSWC and other threats that have yet to be validated and to confirm the detection capabilities for specific pipe attributes. The final objective is to have an array of ILI vendors and technologies to address each threat cost-effectively. The 24-inch diameter string constructed for this project included joints with synthetic and natural features, including SSWC, pits at the SW and in the pipe body, axial and circumferential slots in the pipe body, individual cracks, crack colonies, laminations, wrinkle bends, hard spots, dents with gouges and plain dents. Five ILI systems from five ILI technology providers were tested. The project included the pipe acquisition, the testing string design, the defect creation, the NDE and Lab evaluation of the reference data, the pull-through testing, the ILI vs Reference feature matching, and the statistical analysis of the results. This paper presents the results of this study.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".