The State-of-the-Art Theory and Applications of Best-Estimate Plus Uncertainty Methods
Bibliographic record
Abstract
The approval of the revised rule on the acceptance of emergency core cooling system performance in 1988 triggered a significant interest in the development of codes and methodologies for uncertainty evaluation of best-estimate loss-of-coolant accident (LOCA) analyses. The code scaling, applicability, and uncertainty evaluation method was developed and demonstrated for a large-break LOCA in a pressurized water reactor. Later, several new best-estimate plus uncertainty methods (BEPUs) were developed around the world. The purpose of this paper is to identify and compare the statistical approaches of BEPU methods and present their importance for licensing applications in nuclear power plants. The study showed that the uncertainty analysis with random sampling of input parameters, using the nonparametric statistical tolerance limits for estimating uncertainty of output parameters, is the commonly accepted approach today. The existing BEPU methods seem mature enough, while the future research may be focused on the codes with internal assessment of uncertainty.
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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 | 0.015 | 0.064 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.005 |
| 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".