Nature of the near-field environment in a deep geological repository and the implications for the corrosion behaviour of the container
Bibliographic record
Abstract
The corrosion behaviour of nuclear fuel waste containers depends on the near-field environmental conditions, which may differ significantly from those in the host rock, especially during the early thermal-saturation transient phase. Although it is widely accepted that, in broad terms, the repository evolves from initially warm and oxidising to cool and anoxic in the long term, it is important to understand the detailed nature of the near-field environment in order to predict the corrosion behaviour of the container with confidence. Available information about the near-field environment is briefly reviewed and the expected time dependence of various environmental parameters is defined for a bentonite-backfilled deep geological repository. Although the focus is on a Canadian repository design for copper-coated containers in crystalline or sedimentary host rocks, the discussion is broadly applicable to a variety of container materials and repository designs and locations. Some implications for the corrosion behaviour of the container are also considered.This paper is part of a supplement on the 6th International Workshop on Long-Term Prediction of Corrosion Damage in Nuclear Waste Systems.
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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".