Nuclear Fuel Modelling and Perspectives on Canadian Efforts in Fuel Development
Bibliographic record
Abstract
This chapter discusses how computational efforts span the entire multi-scale multi-physics spectrum, ranging from atomistic electronic structure and classical inter-atomic potential calculations, to meso-scale simulations of micro-structural evolution, to continuum scale thermo-mechanical simulations of fuel performance. A variety of scientific disciplines must be understood in regards to nuclear fuel behavior, including heat transfer, structural and fracture mechanics, thermodynamics, materials science, and reactor physics. A great difficulty in capturing the interdependencies of the foregoing mechanisms is that it is impossible to reproduce the conditions inside of a nuclear reactor in the laboratory. Canadian interests in nuclear fuel research and development have been mainly focused on uranium dioxide ceramic fuels, which are used in CANDU nuclear generating stations, and to a lesser extent in metallic dispersion fuels, which are used in research and test reactors.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.001 |
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".