INVESTIGATION OF THE NEAR-FIELD EVOLUTION IN DEEP GEOLOGICAL REPOSITORIES AND ITS IMPLICATIONS FOR LONG-TERM NUCLEAR WASTE DISPOSAL
Bibliographic record
Abstract
The sustainability of nuclear energy critically depends on the secure management of high-level radioactive waste. Underground multi-barrier waste isolation systems, also known as deep geological repositories (DGRs), are considered the most viable solution for safe and efficient nuclear waste disposal. Significant uncertainties, however, remain regarding the long-term bio-physicochemical evolution of the near-field and its influence on waste isolation performance. In particular, Microbial-Influenced Corrosion (MIC) of copper-coated canisters, driven by the presence of sulphate-reducing bacteria, poses a sparsely constrained threat to repository integrity. This thesis presents an integrative synthesis of existing literature regarding DGR design concepts, near-field components, and evolution. In addition, it presents a process-based reactive transport model simulating MIC mechanisms in a Canadian DGR (crystalline rock). The model integrates microbial population kinetics, sulphate reduction pathways, sulphide generation, copper canister corrosion, and the eventual radionuclide release. A parametric sensitivity analysis was also conducted to evaluate the effect of key reactive transport parameters on sulphide generation and transport. The findings reveal that diffusive transport and microbial sulphate reduction rates are dominant drivers of copper corrosion, with elevated parameter combinations leading to canister failure at approximately 420,000 years. Post-failure simulations, considering pessimistic scenarios with negligible radionuclide inhibition in the host rock, predict rapid release of key radionuclides (notably 135Cs and 129I), governed by their solubility limits and solid phase fractions. This study highlights the importance of coupling biological and geochemical interactions in long-term DGR safety assessments, resulting in the development of a robust and comprehensive safety assessment frameworks for ensuring the long-term safety and integrity of nuclear waste disposal systems.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".