Natural and archaeological analogues for corrosion prediction in nuclear waste systems
Bibliographic record
Abstract
Abstract Natural and archaeological analogues can be a powerful means to build confidence in the long‐term prediction of corrosion in nuclear waste systems. Analogues are meaningful to both experts and lay audiences. Useful analogues exist to support the long‐term behaviour for both copper and steel used fuel containers (UFC) and examples are given for the corrosion processes that are, and are not, expected to occur under repository conditions for both materials. The concept of kinetic versus thermodynamic stability is used to explain the persistence of such analogues. The use of analogues to support the prediction of the long‐term corrosion performance of the UFC in the safety case is also discussed. While there have been a wide range of analogue studies in the past, it is suggested that future studies should be focussed on addressing specific aspects of the treatment of corrosion issues in the safety case.
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How this classification was reachedexpand
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 | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".