Preliminary study on direct recycling of spent BWR fuel in BWR system
Bibliographic record
Abstract
Spent fuel management is considered to be one of the main problems in energy nuclear utilization. Recycling after reprocessing is one of the options for dealing with nuclear reactor spent fuel. Reprocessing is very costly and needs remote handling since spent fuel is very hazard high level waste. On top of that, only a small number of countries can manage a reprocessing plant. If country likes Indonesia decide to "go nuclear", it should find another way to deal with the nuclear spent fuel. Korea has proposed the DUPIC (Direct Utilization of Spent PWR fuel In CANDU) concept. Nevertheless, DUPIC concept requires two types of nuclear power plants, i.e., pressurized water reactor (PWR) and CANadian Deuterium Uranium reactor (CANDU). In this study, we evaluate a scheme of direct recycling of spent BWR fuel in BWR system, under the concept that we have called as a SUPEL (Straight Utilization of sPEnt LWR fuel in LWR system) scenario. Several spent BWR fuel compositions in loaded BWR fuel has been evaluated to achieve the criticality of reactor.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".