Validity of the LAPA Method for Assessment of Defects Reported by In-Line Inspection Tools
Bibliographic record
Abstract
Over recent years, RSTRENG has gained acceptance as a reliable method of assessing the effect of pipeline corrosion defects, while reducing the conservatism inherent in some of the alternative assessment models. The Length Adaptive Pressure Assessment (LAPA) algorithm has been developed to apply an adaptation of the RSTRENG methodology directly to the results of a magnetic in-line inspection. Adoption of the LAPA technique offers substantial accuracy and efficiency benefits over the more conventional processes, but the industry requires confirmation of the method’s validity before it can take advantage of these. The paper will describe the LAPA validation work performed by PII, and independent validation undertaken by TransCanada Pipelines and other pipeline operators. The independent validation took the form of a comparison of LAPA-based burst pressure calculations with RSTRENG calculations based on direct infield measurements of the defect profile. TransCanada also performed a series of burst tests to quantify the accuracy of the LAPA calculations.
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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".