A Mixed-Potential Model to Predict Fuel (Uranium Dioxide) Corrosion within a Failed Nuclear Waste Container
Bibliographic record
Abstract
A mixed-potential model is described to predict the corrosion behavior of used nuclear fuel inside a steel-lined failed Canadian nuclear waste container under anticipated waste vault (repository) conditions. The model accounts for the effects of the alpha radiolysis of water, the precipitation of corrosion products on both the fuel and the carbon steel (CS), and redox reactions between species produced by either radiolysis or corrosion at the fuel surface and by corrosion on the CS liner. The model is based on a series of ten one-dimensional reaction-diffusion equations, each describing the mass-transport, precipitation/dissolution, adsorption/desorption, and redox processes of the ten chemical species included in the model. These equations are solved using finite-difference techniques. A three-layer spatial grid is used, with the two outer layers (of time-varying thickness) representing porous precipitated corrosion products on the uranium dioxide (UO2) and CS surfaces. The middle layer represents a layer of groundwater solution in the saturated failed containers. Electrochemical rate expressions are used as boundary conditions for species that participate in interfacial electrochemical reactions.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".