Computation of a Canadian SCWR unit cell with deterministic and Monte Carlo codes
Bibliographic record
Abstract
The Canadian SCWR has the potential to achieve the goals that the generation IV nuclear reactors must meet. As part of the optimization process for this design concept, lattice cell calculations are routinely performed using deterministic codes. In this study, the first step (self-shielding treatment) of the computation scheme developed with the deterministic code DRAGON for the Canadian SCWR has been validated. Some options available in the module responsible for the resonance self-shielding calculation in DRAGON 3.06 and different microscopic cross section libraries based on the ENDF/B-VII.0 evaluated nuclear data file have been tested and compared to a reference calculation performed with the Monte Carlo code SERPENT under the same conditions. Compared to SERPENT, DRAGON underestimates the infinite multiplication factor in all cases. In general, the original Stammler model with the Livolant-Jeanpierre approximations are the most appropriate self-shielding options to use in this case of study. In addition, the 89 groups WIMS-AECL library for slight enriched uranium and the 172 groups WLUP library for a mixture of plutonium and thorium give the most consistent results with those of SERPENT. (authors)
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".