METHODOLOGY FOR SELECTING SSC FOR TIME-DEPENDENT RELIABILITY MODELLING IN PSA
Bibliographic record
Abstract
Current Probabilistic Safety Assessments (PSA) performed worldwide do not model effects of ageing on performance of Systems, Structures, and Components (SSC). In part, this is explained by the lack of mature time dependent reliability assessment methodologies. Implementation of such methodologies promises significant benefits from optimizing risk management based on a better understanding of risk profile evolution during the plant life and variation of importance measures with age. Recognizing this, the Canadian Nuclear Safety Commission (CNSC) started in 2006 a research project Incorporating Ageing Effects into Based on this project, a methodology has been developed for selection of SSCs ageing of which should be explicitly modelled in PSA. Taking into account the high resource intensity of the timedependent reliability modelling, it is important to ensure that the relative priorities are established and the most risk-significant effects are modelled in the first place. This has a considerable impact on the overall practicality of the exercise. The methodology defines objectives of the study, identifies potential applications, establishes a set of criteria to minimize the subjectivity of decisions, and provides a systematic approach for producing a ranked list of SSCs. The methodology is technology neutral, adaptable and may be useful for regulators, utilities, and designers.
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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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".