Detection and Verification of SCC in a Gas Transmission Pipeline
Bibliographic record
Abstract
Two sections of a 914mm OD (36 in.) TransCanada (TCPL) gas transmission pipeline (predominantly with 9.14 mm wall thickness) were inspected using an ultrasonic liquid coupled crack detection In-Line Inspection (ILI) tool. One of the objectives of the inspection was to establish the condition of the pipeline sections with a known history of stress-corrosion cracking (SCC). Under test was the practicability of inspecting a gas line using a liquid coupled ILI tool, specifically its ability to detect and size defects deeper than 1 mm and distinguish cracks and crack-like defects from other types of anomalies, such as inclusions and laminations. In order to assess the confidence level of the tool, both sections were inspected in two independent runs and the repeatability of inspection was assessed. Cracks and crack-like defects with depths greater than 12.5% of the wall thickness from both runs were compared and correlation was established to assess repeatability. The accuracy of tool predictions was verified in excavations in both sections. 40 reported features, varying in depths up to over 40% were examined with respect to location, type, and size. Examples of defect patterns are shown to demonstrate the accuracy of the inspection method.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".