OSCAR-4 CODE SYSTEM COMPARISON AND ANALYSIS WITH A FIRST-ORDER SEMI-EMPIRICAL METHOD FOR CORE-FOLLOW DEPLETION CALCULATION IN MNR
Bibliographic record
Abstract
Knowledge of the isotopic composition of a nuclear reactor core is important for accurate core-follow and reload analysis. In the McMaster Nuclear Reactor, fuel depletion estimates are based upon a semi-empirical calculation using flux-wire measurements. These estimates are used to plan and guide fuelling operations. To further support operations, an OSCAR-4 model is being developed. To evaluate the performance of the OSCAR-4 code for this application, 2 points of comparison, considering the period between 2007 and 2010, are presented: (i) the multiplication factor keff and (ii) U-235 fuel inventory. The latter is compared with a simple first-order semi-empirical calculation. The calculation of keff for the last operational 3 months yields 0.997 ± 0.002 (vs. 1.000 for an operating reactor), and differences in both core-average inventory and the maximum standard fuel assembly inventories estimates are found to be 5.7% and 7.5%, respectively.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".