Considerations for the Use of Probabilistic Assessments in Regulatory Decision Making Related to Pressure Boundary Component Aging
Bibliographic record
Abstract
Within the current Canadian regulatory framework, the structural integrity of pressure boundary components with detected service-induced degradation must be demonstrated using deterministic evaluation techniques. However, Canadian Nuclear Safety Commission staff has recognized that the inherent conservatism in these deterministic assessment approaches may generate overly conservative conclusions when they are applied to assess the impact of postulated service-induced degradation to establish aging management requirements for nuclear power plant pressure boundary components. This may have the unintended effect of reducing the effectiveness of aging management programs by directing resources towards activities that will have minimal benefit on improving plant safety and could result in unnecessary dose to personnel. With this in mind, CNSC staff has accepted the limited use of probabilistic assessments prepared by licensees to support aging management activities for pressure boundary components. These probabilistic assessments form a part of risk-informed decision making strategies intended to reduce excess conservatism that could arise if decisions are based solely on the results of deterministic assessments. This paper provides an overview of CNSC staff’s experiences with the review and acceptance of licensee submissions incorporating probabilistic assessments of pressure boundary component aging for risk informed decision making.
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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 | 0.393 | 0.529 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.008 | 0.005 |
| Science and technology studies | 0.006 | 0.011 |
| Scholarly communication | 0.020 | 0.013 |
| Open science | 0.008 | 0.007 |
| Research integrity | 0.006 | 0.011 |
| Insufficient payload (model declined to judge) | 0.004 | 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".