Bayesian Analysis of Piping Leak Frequency Using OECD/NEA Data
Bibliographic record
Abstract
The estimation of piping failure frequency is an important task to support the probabilistic risk analysis and risk-informed in-service inspection of nuclear power plant systems. This paper describes a hierarchical or two-stage Poisson-gamma Bayesian procedure and applies this to estimate the failure frequency using the Organization for Economic Co-operation and Development/Nuclear Energy Agency pipe leakage data for the United States nuclear plants. In the first stage, a generic distribution of failure rate is developed based on the failure observations from a group of similar plants. This distribution represents the interplant (plant-to-plant) variability arising from differences in construction, operation, and maintenance conditions. In the second stage, the generic prior obtained from the first stage is updated by using the data specific to a particular plant, and thus a posterior distribution of plan specific failure rate is derived. The two-stage Bayesian procedure is able to incorporate different levels of variability in a more consistent manner.
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How this classification was reachedexpand
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".