Bibliographic record
Abstract
Aging gas and oil transmission pipeline infrastructure has led to the need for improved integrity assessment. Presently, external and internal corrosion defects are the leading cause of pipeline failure in Canada, and in many other countries around the world. The currently accepted defect assessment procedures have been shown to be conservative, with the degree of conservatism varying with the defect dimensions. To address this issue, a multi-level corrosion defect assessment procedure has been proposed. The assessment levels are organized in terms of increasing complexity; with three-dimensional elastic-plastic Finite Element Analysis (FEA) proposed as the highest level of assessment. This method requires the true stress-strain curve of the material, as determined from uniaxial tensile tests, and the corrosion defect geometry to assess the burst pressure of corrosion defects. The use of non-linear FEA to predict the failure pressure of real corrosion defects has been investigated using the results from 25 burst tests on pipe sections removed from service due to the presence of corrosion defects. It has been found that elastic-plastic FEA provides an accurate prediction of the burst pressure and failure location of complex-shaped corrosion defects. Although this approach requires detailed information regarding the corrosion geometry, it is appropriate for cases where an accurate burst pressure prediction is necessary.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".