Bibliographic record
Abstract
Pipeline dents are common occurrences that have a potential integrity threat to the system. Dents are typically found through in-line inspections, and historically, low-resolution in-line inspection geometry tools were used to find the locations of dents. These tools gave little information about shape, orientation or other dent features. Newer ‘high-resolution’ tools give a much clearer picture of the dent shape, location, orientation and location of welds. This information has been previously unavailable and has enabled dent integrity assessment with much greater accuracy and confidence. However this still leaves the question of how to best address the information from older, low-resolution inspection tools. In the past, CSA Z662 required that all dents with a deflection greater than 6% or that contained stress concentrators, including welds, had to be repaired. In the newly published 2003 edition of CSA Z662, dents can be assessed by an engineering assessment to determine their acceptability. Historical evidence has shown that dents less than 6% can also be subject to failure under certain conditions, and is indicated in the notes of CSA Z662-03 10.8.2.4.2. Dents that contain stress concentrators, including corrosion, welds and cracks must be given special consideration, however often little information is available for the dent from solely a geometry tool. TransCanada PipeLines Limited has been involved in the development of a dent assessment methodology for several years. Based on the 2003 revisions to CSA Z662, TransCanada has started to implement a dent integrity management program. This paper discusses the approach taken by TransCanada: to create a database of dent features, classification of dents, finite element analysis (FEA) to determine cyclic stress spectra, fatigue analysis, validation through dig programs, and the management of these features from a system integrity standpoint.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.041 | 0.011 |
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".