Use of ECDA Approach in Prioritization of ILI Anomalies
Bibliographic record
Abstract
Abstract An effective In-line Inspection (ILI) program must provide a prioritized excavation response plan to address anomalies identified as being of particular concern. Locations that show potential for imminent or short term failure are prioritized under Phase 1 and Phase 2 responses respectively. Anomalies that could grow to become a severe risk for pipeline integrity prior to the next ILI are prioritized as Phase 3 excavations. This paper describes the use of External Corrosion Direct Assessment (ECDA) principles in prioritizing Phase 3 anomalies on a gas pipeline in northern Ontario, resulting in a more effective excavation program. A type of ECDA prioritization criterion, based on the results of an integrated Close Interval Potential Survey/Direct Current Voltage Gradient (CIPS/DCVG) survey in conjunction with the results of Phase 1 and Phase 2 digs, is proposed.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".