A Benchmark Study on Risk-Informed In-Service Inspection Methodologies (RISMET)
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Between 2005 and 2009 the Joint Research Centre of the European Commission (JRC), together with the Nuclear Energy Agency of the Organisation for Economic Co-operation and Development (OECD/NEA), coordinated a project for benchmarking various risk-informed in-service inspection (RI-ISI) methodologies. In the project, called RISMET, various RI-ISI methodologies were applied to the same case, consisting of four selected piping systems at the Swedish nuclear power plant Ringhals Unit 4, a 3-loop Westinghouse Pressurised Water Reactor (PWR). The RI-ISI applications were compared among each other and to the deterministic ASME section XI ISI selection procedure. More than twenty organizations from Europe, U.S., Canada and Japan took part.\nThe scope of the benchmark was limited to four piping systems but the variety regarding safety class, potential degradation mechanisms and pipe break consequences ensured a good coverage of issues for a comparative study. The risk-informed methodologies showed some significant differences and resulted in slightly different risk ranking and selection of inspection sites. However, the results of the benchmark indicated that the risk impact of these differences is small, and the RI-ISI approaches identify safety important piping segments that are ignored by approaches not using the probabilistic safety assessment (PSA). The results of the benchmark exercise RISMET improve the knowledge on differences in approaches and their impact on plant safety, and promote the use of risk-informed ISI.\nThis paper summarizes the RISMET benchmark project and its main results.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.046 | 0.060 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.004 |
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 it