PR-214-153739-WEB ERW Fatigue Life Integrity Management Improvement - Phase III
Bibliographic record
Abstract
Tuesday, September 17, 2019 11:00 a.m ET PRESENTER: Aaron Dinovitzer, MA Sc., MBA, PEng, BMT Canada HOST: Mark Piazza, Colonial Pipeline Company MODERATOR: John Lynk, PRCI CLICK THE BUY/DOWNLOAD BUTTON TO ACCESS THE WEBINAR REGISTRATION LINK Join the PRCI Integrity and Inspection Technical Committee as they present research results that set out to evaluate the level of conservatism inherent to current state-of-practice procedures using full-scale fatigue testing. Learning outcomes/benefits of attending: - Understand the sources of conservatism, and how to improve the accuracy of, fatigue life estimation techniques for ERW or EFW pipes containing manufacturing process induced crack-like features. - Learn about an improved estimation method of remaining pipe fatigue life estimates that better agree with actual operational experience. This can reduce conservatism in engineering fatigue life estimates over currently used techniques. - Since fatigue life estimates are used to identify inspection intervals and assessing the life of features in a pipeline system, reducing unnecessary conservatism will avoid the cost of unnecessary inspections, pressure tests and repairs before fatigue life is expended. Who should attend? - Pipeline integrity and risk personnel, engineers and management Recommended pre-reading: PR-214-153739-R01 ERW Fatigue Life Integrity Management Improvement - Phase III Register anyway to automatically receive a link to the webinar recording to view on-demand at your convenience. After registering, you will receive a confirmation email containing information about joining the webinar.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.778 | 0.691 |
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".