A sensitivity study on the PDFs treating uncertainties in severe accidents for pressurized heavy water reactors
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Bibliographic record
Abstract
This research article introduces a study regarding the uncertainties treatment during severe accidents for Pressurized Heavy Water Reactors (PHWRs). The present study is focused upon the unmitigated Station BlackOut (SBO) accident analysis for a CANada Deuterium Uranium (CANDU) type reactor emphasizing the impact of the uncertainties treatment on the relevant key timings of the SBO accident progression through different approaches for the uncertainty parameters’ Probabilistic Distribution Functions (PDFs). A comparison between the sensitivity analysis results is provided in the present research study. The uncertainty analysis is performed with the RELAP/SCDAPSIM code with the Integrated Uncertainty Analysis (IUA) package from the code. Results from the research would support the advancements on the best-practices for uncertainty analyses with respect to the parameter’s uncertainties distribution functions. Data dispersion is a key element for the realistic quantification of uncertainties in nuclear power plant safety analyses, including severe accidents.
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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) | 0.000 | 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 it