Capability of Mathematical Probability Tables to Treat Resonance Interference among Isotope
Bibliographic record
Abstract
In light water reactors, it is very important to consider the resonance self-shielding behavior of the cross-section, where the accuracy of the calculation method is related to the technique used to represent the self-shielding distribution. The impact of the self-shielding on one nuclide cross-section can depend strongly on the resonance cross-section of other nuclides in the composition, accordingly, the mutual resonance interference between different resonance isotopes should be considered. The advanced subgroup technique based on the mathematical probability tables can treat such effect, where the Ribon extended model (RIB) and the subgroup projection method (SPM) are available in DRAGON4 code. The calculations are performed for three PWR fuel types: UO2, ThO2-UO2, and PuO2-UO2, and the obtained results are benchmarked with the reference MCNP6 calculations. The main purpose of these studies is to investigate which isotopes can be included in the correlation model and the results indicate that the contribution of some isotopes may disturb other cross-sections leading to discrepancy from MCNP results. Consequently, those isotopes should be removed from the correlation model especially for the PuO2-UO2 fuel pin.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".