Deterministic Leak-Before-Break Treatment of Uncertainties: Part 1 – Theoretical Basis
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
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. This paper describes a systematic approach for quantifying the uncertainty in the CAA/DLBB assessment. The proposed approach integrates 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. A brief description of the CAA/DLBB evaluation process is presented and the leak-rate factor is adopted as the figure of merit. Next, the key leak-rate effect factors are identified and the uncertainty in each is established. This is followed by a description of two statistical approaches that can be used to propagate the uncertainties for the bounding postulated break location and to determine the level of uncertainty associated with the CAA/DLBB assessment. In Part 2 of the paper, the application of the approach is illustrated.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it