Characterization of Topside Mechanical Damage
Bibliographic record
Abstract
Mechanical damage, such as gouge, is the damage to the pipe surface caused by external forces and is usually caused by third-party damage during construction and excavation. This normally results in a highly deformed, work hardened surface layer with possible metal removal. In many cases, dents are coincident with gouges. Industry standards and regulators treat this type of mechanical damage as critical and require immediate investigation. Therefore, from a pipeline operator perspective, distinguishing between plain dent and dent with gouge is a great challenge for topside dents because quite often they are caused by un-authorized third party activity and contain gouge. In a previous study[1,2], the present authors developed an approach that combines dent strain-severity criterion with MFL signal recognition to identify dent with gouge and crack. In this paper, an extension of the previous study to topside dents is presented. The enhanced approach for distinguishing between plain dents and dent with gouge/crack for topside dents is summarized. Case studies are given to demonstrate the effectiveness of the approach for identify topside dent with gouge.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 teacher head, 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".