Performance of Advanced Self-Shielding Models in DRAGON for the Estimation of CANDU-6 Safety Parameters
Bibliographic record
Abstract
The effect of advanced resonance self-shielding models incorporated in the developmental version of the DRAGON code on estimation of reactivity coefficients of a typical CANDU-6 lattice is evaluated. The advanced self-shielding models are based on either equivalence in the dilution model or on a subgroup approach. Under equivalence in dilution models, the generalized Stamm’ler model was used with or without Riemann integration and Nordheim model. Among the subgroup approaches, the Ribon extended and the statistical self-shielding models were used. The Ribon extended self-shielding model uses mathematical probability tables, while the statistical self-shielding model uses physical probability tables. The analysis focused on four important transients, which include the fuel temperature coefficient, coolant void reactivity, pressure tube ingression, and calandria tube ingression. Four burnup stages for estimation of reactivity have been identified. To benchmark the results obtained using DRAGON, the results obtained were compared with those of MCNP5. These analyses indicated that, of all the self-shielding models, the resonance self-shielding model based on the subgroup approach using physical probability tables seems to perform well for all situations and can be recommended for CANDU-6 analyses using the code DRAGON.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".