The Use of Probabilistic Safety Techniques for Evaluating the Advanced CANDU Reactor (ACR)
Bibliographic record
Abstract
THAT WAS SUBMITTED The U.S. Nuclear Regulatory Commission (NRC) is anticipating licensing applications for reactor facilities that are significantly advanced beyond the current generation of operating reactors. One proposed reactor design, developed by Atomic Energy of Canada, Limited (AECL), is an Advanced CANada Deuterium Uranium (CANDU) Reactor (ACR), the ACR-700. The ACR is an enhanced version of earlier CANDU designs. However, unlike the CANDU reactors, which are heavy-water cooled and moderated reactors, the ACR-700 is a light-water cooled and heavy- water moderated reactor. In preparation of a possible design certification review, the NRC (with the assistance of ORNL) began examining selected areas of nuclear safety, identifying accidents that could potentially dominate the risk profile of the ACR-700 design, and evaluating other risk-important design and technology issues. This effort supports the NRC's policy that encourages the use of probabilistic risk assessment (PRA) in all regulatory matters. In addition to identifying potential initiating events and systems judged to be important to preventing and mitigating possible accident conditions, a prototype risk evaluation model for the ACR-700 was developed using the SAPHIRE computer code.
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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 | 0.009 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".