Gas Generation and Migration in Deep Geological Radioactive Waste Repositories
Bibliographic record
Abstract
The option of disposal nuclear waste in a deep geological repository (DGR) is currently being studied in several countries (e.g., Canada, China, France, Germany, India and Switzerland). The long-term performance of a DGR in rock generally relies on the protection of multiple barriers, including engineered and natural barriers. Significant amounts of gases could be generated in DRGs from several processes, such as degradation of waste forms or corrosion of waste containers. These gases could migrate through both engineered and natural geologic barrier systems. The increased pressure of the gases, if large enough, could cause microcracks or macrocracks to form, affecting the integrity of the barriers and the geosphere as a barrier to long-term contaminants. In addition, these gases could have a significant impact on the biosphere and groundwater. Thus, assessing the long-term safety of a nuclear waste repository in a deep geological formation requires a good understanding of the mechanisms of gas migration, the prediction of the gas migration as well as their effects on the integrity, mechanical (M) and hydraulic (H) stability of the repository. In this keynote lecture, the mechanisms of gas generation and transport in DGRs for radioactive waste will be discussed. In addition, techniques for modeling and predicting gas transport in GDRs will be presented. Finally, modeling studies of gas migration in a potential Canadian DGR will be addressed.
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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 | 0.000 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".