BEMUSE Phase V Report Uncertainty and Sensitivity Analysis of a LBLOCA in Zion Nuclear Power Plant. OECD/NEA Report
Bibliographic record
Abstract
The BEMUSE (Best Estimate Methods – Uncertainty and Sensitivity Evaluation) Programme has been promoted by the Working Group on Accident Management and Analysis (WGAMA) and endorsed by the Committee on the Safety of Nuclear Installations (CSNI). \n \nThe high-level objectives of the work are: \n \n To evaluate the practicability, quality and reliability of Best-Estimate (BE) methods including uncertainty and sensitivity evaluation in applications relevant to nuclear reactor safety \n \n To develop a common understanding in this domain \n \n To promote and facilitate their use by the regulatory bodies and the industry \n \nOperational objectives include an assessment of the applicability of best-estimate and uncertainty and sensitivity methods to integral tests and their use in reactor applications. \n \nThe scope of the programme is to perform Large Break Loss-Of-Coolant Accident (LB-LOCA) analyses making reference to experimental data and to a Nuclear Power Plant (NPP) to address the issue of “the capabilities of computational tools” including scaling and uncertainty analysis. \n \nThis report is focused on BEMUSE Phase V activities and results. In Phase I the methodologies were discussed, in Phase II the Best-Estimate calculation of a test was performed, in Phase III the uncertainties and sensitivities were analyzed for the previous test and finally, in Phase IV, the Best Estimate calculation for a NPP was developed. All these previous phases constitute the background which is intended to be used in the present phase in order to produce final uncertainty results. Nowadays, Best Estimate Plus Uncertainty Methods are broadly used worldwide, directly for licensing purposes (USA, Netherlands, Brazil, etc.) or linked to future use for licensing (Canada, Czech Republic, France, etc.). The results presented in this report conclude on the computational aspects of the comparative exercise as they are a necessary step for future uses of these methods for licensing purposes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.020 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.017 | 0.002 |
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".