Deterministic Leak-Before-Break Treatment of Uncertainties: Part 2 – Example Application
Bibliographic record
Abstract
Abstract The Canadian CANDU® Industry has developed and is implementing a composite analytical approach (CAA) to demonstrate, with high confidence, appropriate safety margins for deterministic nuclear safety analysis of large-break loss-of-coolant accident events in existing CANDU reactors. A key element of the CAA is the deterministic leak-before-break (CAA/DLBB) assessment for postulated through-wall cracks in all butt welds in the large-diameter primary heat transport system piping and reactor headers. The companion paper proposes an approach to integrate the uncertainties of the key leak-rate parameters into a single metric (a quantitative indicator of the uncertainty in the CAA/DLBB assessment, a.k.a., performance indicator). This approach ensures that the uncertainty in individual key parameters is not taken out of the context of the uncertainty of the other key parameters. This paper illustrates the application of this systematic approach for quantifying the uncertainty in the CAA/DLBB assessment to a specific postulated break location. The example application illustrates how the bounding values of the key leak-rate factors are quantified and how the uncertainties in the factors are integrated using the analytical and numerical approaches and discuss how the proposed approach provides an unbiased estimate of the leak-rate factor for the postulated break location that is assessed.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".