Natural and Anthropogenic Analogues for High-Level Nuclear Waste Disposal Repositories: A Review
Bibliographic record
Abstract
ABSTRACT Projects involving deep geological disposal of nuclear waste are unique in that predictive models of long-term safety often involve geological timeframes. This manuscript provides a review of natural and anthropogenic analogues for high-level nuclear waste disposal in a deep geological repository. We also occasionally highlight analogues that have been used for low- and intermediate-level waste. Most studies define natural analogues as either naturally occurring or anthropogenic systems. In this paper, we distinguish between natural analogues and anthropogenic analogues because the latter generally provide non-technical (anecdotal) illustrations of concepts and processes for the safety case, whereas the former can provide technical and quantitative information. In addition, natural analogues can provide information over geological time scales (millions of years) and spatial scales (kilometers), whereas anthropogenic analogues provide information over a much more limited time scale (hundreds or thousands of years). Regardless of the definition, analogue studies provide one of the multiple lines of evidence intended to increase confidence in the safe geological disposal of high-level nuclear waste. They are deemed necessary because they complement the experiments that are carried out over a period of months or years. They also provide a way to validate numerical long-term safety assessment models with information and data covering geological time scales and spatial scales. The first part of this review describes the analogue concept. The second and third parts provide examples of natural and anthropogenic analogues for engineered barrier systems and natural barriers. Part four describes analogues for complex coupled transport processes. Finally, we discuss general and specific areas of future research. A concerted effort should be made to ensure that there is a transfer of data from the complex, natural analogue field studies to simplistic models which, by necessity, are used to evaluate the long-term safety of deep geological repositories. Field analogue studies should be planned to interface with laboratory experiments and, ultimately, with in situ field experiments, when the final repository site is selected. This will involve using natural analogue data in a quantitative way to support the deep geological repository safety case.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".