Probabilistic model for pitting of copper canisters
Bibliographic record
Abstract
Abstract Copper canisters for the final disposal of spent nuclear fuel are expected to be subject to general (uniform) corrosion and a limited degree of localised damage. Although the canister is expected to be exposed to predominantly active conditions, it is nevertheless prudent to assess the probability of pitting corrosion in passive conditions. A probabilistic pitting model is developed for aerobic conditions in water‐saturated bentonite. While aerobic, saturated conditions may be unlikely to occur if saturation is slow or O2 consumption is fast, these conditions have been assumed as a starting point because of the availability of data. The model samples from environmental conditions use a Monte Carlo approach to predict active or passive conditions. Under passive conditions, the model compares corrosion potentials to breakdown and repassivation potentials to determine pit initiations and deaths, respectively. Pit depth distributions can then be calculated. For the assumed conditions, the model predicts that active conditions dominate 90% of the time while the remaining passive conditions can lead to pitting during the first few years primarily controlled by the availability of oxygen.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| 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 | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".