COG Risk-Informed In-Service Inspection (RI-ISI) Project
Bibliographic record
Abstract
In 2009, the Canadian Nuclear Safety Commission (CNSC) prepared “Guidelines for Risk-Informed In-service Inspection for Piping,” which provided guidance and acceptance criteria for licensees to develop an RI-ISI program as an alternate to the current CSA N285.4 and augmented programs for piping inspection. A project was initiated by the CANDU Owners Group (COG) to develop a best-fit RI-ISI methodology for the CANDU design and evaluate plant risk levels between current and RI-ISI inspection programs. The traditional EPRI RI-ISI methodology was selected as a starting point, and four plant systems were evaluated. Both failure potential and consequence of failure were used to establish the risk significance for all in-scope piping components. Once the risk associated with each component was established, elements were selected for inspection based upon the sampling percentages of the EPRI RI-ISI methodology, and as a final check a comparison was made between plant risk under the current CSA N285.4 (and augmented) inspection program and under RI-ISI. The project was successfully concluded in 2011, and results confirmed that the EPRI RI-ISI methodology can be adapted to the CANDU design and the degradation mechanisms evaluated under RI-ISI are consistent with CANDU operating experience. The original CSA N285.4 basis for the CANDU Periodic Inspection Program (PIP) was validated, and potential improvements to the station inspection programs were identified.
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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 | 0.011 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.002 | 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".