Coupled CLASS and DONJON5 3D full-core calculations and comparison with the neural network approach for fuel cycles involving MOX fueled PWRs
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Bibliographic record
Abstract
The scenario code CLASS relies on infinite assembly simulation to predict fuel actinide inventories at exit burnup. In the current work, we replace these assembly calculations by full-core simulations and evaluate the impact on actinide inventories predicted by CLASS. To achieve this goal, we generate neural network training databanks for CLASS using the lattice code DRAGON5. For UOX fuels, the databanks are sampled stochastically for exit burnup, moderator boron concentration and uranium 235 enrichment while for MOX fuels an eight-dimensional grid is sampled that also accounts for plutonium and americium-241 initial contents. DRAGON5 is used to generate the databases for DONJON5 3D full-core diffusion calculations in CLASS. Results obtained using neural networks CLASS and DONJON5/CLASS calculations are then compared to assess the different assumptions used in classical scenario simulations and determine the major source of errors. A simple UOX scenario involving long-term fuel storage and a more complex scenario involving reprocessed UOX spent fuel and MOX fabrication are then studied. They show that inventories of uranium 235 and minor actinides are sensitive to full-core simulations. Moreover, the neural networks CLASS simulations can be improved using an adapted kthreshold that depends on the initial fuel composition.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it