Geometric Dent Characterization
Bibliographic record
Abstract
The Canadian pipeline design standard (CSA Z662) requires the repair of smooth dents with depths exceeding 6% of the pipeline’s outside diameter. This limit on dent depth is reduced in the presence of additional localized effects such as pipe wall gouges, corrosion, planar flaws or weld seams. It has been noted, however, that pipelines have operated satisfactorily with dents in excess of 10% while others with 3% dents have failed. Based upon observation of this type the question arises, “Is there more to characterizing a dent than its depth?” An ongoing group sponsored project at BMT Fleet Technology Limited (FTL) is exploring the issue of dent characterization using a dent assessment model developed at FTL. The objective of this project is to develop a rapid dent life expectancy characterization technique based upon dent geometry, line pressure history and line pipe material properties. This paper will outline the general characterization approach being considered and demonstrate some of the observed and expected relationships between service life and dent geometry. The relative importance of each dent characteristic (geometric measures, line pipe material and line pressure history) will be discussed to demonstrate the potential of the rapid characterization approach being developed.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".