Computer Code Applicability Assessment for the Advanced CANDU Reactor
Bibliographic record
Abstract
AECL Technologies (AECLT), the 100%-owned US subsidiary of Atomic Energy of Canada Ltd. (AECL), is currently the proponents of a pre-licensing review of the Advanced CANDU® Reactor (ACR™)* with the United States Nuclear Regulatory Commission (USNRC). A key focus topic for this pre-application review is the NRC acceptance of the computer codes used in the safety analysis of the ACR. These codes have been developed and their predictions compared against experimental results over extended periods of time in Canada. These codes have also undergone formal validation in the 1990’s. In support of this formal validation effort AECL has developed, implemented and currently maintains a Software Quality Assurance program (SQA) to ensure that its analytical, scientific and design computer codes meet the required standards for software used in safety analyses. This paper discusses the SQA program used to develop, qualify and maintain the computer codes used in ACR safety analysis, including the current program underway to confirm the applicability of these computer codes for use in ACR safety analyses.
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 | 0.014 | 0.088 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".