A Spent-Fuel Combination Method for Minimizing Composition Variability of DUPIC Fuel
Bibliographic record
Abstract
A combination method of spent pressurized water reactor (PWR) fuel is proposed that adjusts the fuel composition for direct use of spent PWR fuel in Canada deuterium uranium (CANDU) reactors (DUPIC). This method reduces the composition heterogeneity (variation) of the DUPIC fuel caused by directly reusing spent PWR fuel as DUPIC fuel feedstock. In this study, a combination method was used to find the optimum mixture composition from the spent PWR fuel database by minimizing the composition variation of the major fissile isotopes 235U and 239Pu. The simulation results have shown that the combination method can reduce the composition variation of 235U and 239Pu to 0.11 and 1.40%, respectively, through assemblywise mixing operation only. It is also believed that the result could be improved further through a rodwise combination technique if the isotopic composition of each spent PWR fuel rod is known by direct measurement during the DUPIC fuel fabrication process.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".