Benchmarking PRAISE-CANDU 1.0 With NURBIM Project Fatigue Cases
Bibliographic record
Abstract
A probabilistic fracture mechanics code, PRAISE-CANDU 1.0, has been developed under a software quality assurance program in full compliance with Canadian Standards Association (CSA) N286.7-99, and was initially released in June 2012. Extensive verification and validation has been performed on PRAISE-CANDU 1.0 for the purpose of software quality assurance. This paper presents the fatigue benchmarking against NURBIM (NUclear Risk Based Inspection Methodology for passive components) fatigue cases between PRAISE-CANDU 1.0 and six other Probabilistic Fracture Mechanics (PFM) codes. This benchmarking is considered to be an important element of the validation of PRAISE-CANDU. Excellent agreement is observed in spite of the differences between the codes. The comparison of the predicted leak probability at the 40th year shows that PRAISE-CANDU not only captures the same trend but also bounds (higher predicted failure probability) the majority of the NURBIM results. In addition to the leak probability, the rupture probability and uncertainty analysis, which were not reported in the NURBIM Project, are also calculated with PRAISE-CANDU and presented.
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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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".