Advances and Research Needs in Risk-Informed In-Service Inspection Methodology
Bibliographic record
Abstract
While risk-informed in-service inspection (RI-ISI) program has been applied in several countries to enhance the traditional periodic inspection program (PIP), many other countries are waiting for more successful implementation experiences to be accumulated. Canadian Nuclear Safety Commission (CNSC), the regulatory body of nuclear industry in Canada, became increasingly interested in the risk-informed decision making methodology. Several small-scale pilot studies on RI-ISI have been initiated by Canadian utilities during the past few years. Nevertheless, a RI-ISI methodology appropriate for the CANDU technology that can be accepted by the stakeholders has yet to be developed. The development of the RI-ISI methodologies derived from the PWR/BWR operating experiences is first reviewed, followed by an examination of Canadian periodic inspection standard CSA N285.4 and its evolution from a RI-ISI perspective. Finally several key technical issues and research needs in developing an advanced RI-ISI methodology for nuclear power plants are 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 | 0.055 | 0.062 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.006 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| 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".