CANDU Owners Group Risk-Informed In-Service Inspection (RI-ISI) Project
Bibliographic record
Abstract
The Canadian Nuclear Safety Commission (CNSC) has prepared “Guidelines for Risk-Informed In-service Inspection (RI-ISI) for Piping.” This document provided instructions and acceptance criteria for licensees to develop an RI-ISI program integrating both risk insights and traditional analysis as an alternate to the current programs for piping inspection. In response to the CNSC guideline, a CANDU Owners Group (COG) project was initiated to develop a best-fit methodology for nuclear systems within the CANDU design. The traditional EPRI RI-ISI methodology was selected as a starting point for the CANDU best-fit methodology development. Since characterization of risk requires knowledge of both failure potential and the consequence of failure, these values were determined for in-scope piping welds. Once the risk associated with each component was established, the components were ranked accordingly, elements were selected based upon the sampling percentages of EPRI RI-ISI, and a comparison was made between plant risk under the current CSA N285.4 / augmented programs and RI-ISI. Application to a number of nuclear and non-nuclear systems has been completed and has shown where inspection allocation can be improved and where low-value added inspections can be reduced. Work is underway to extend the results of this project to conventional systems and components beyond piping welds.
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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 | 0.030 | 0.021 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".