Use of groundwater lifetime expectancy for the performance assessment of a deep geologic radioactive waste repository: 2. Application to a Canadian Shield environment
Bibliographic record
Abstract
F. J. Cornaton et al. (2008) introduced the concept of lifetime expectancy as a performance measure of the safety of subsurface repositories, on the basis of the travel time for contaminants released at a certain point in the subsurface to reach the biosphere or compliance area. The methodologies are applied to a hypothetical but realistic Canadian Shield crystalline rock environment, which is considered to be one of the most geologically stable areas on Earth. In an approximately 10 × 10 × 1.5 km3 hypothetical study area, up to 1000 major and intermediate fracture zones are generated from surface lineament analyses and subsurface surveys. In the study area, mean and probability density of lifetime expectancy are analyzed with realistic geologic and hydrologic shield settings in order to demonstrate the applicability of the theory and the numerical model for optimally locating a deep subsurface repository for the safe storage of spent nuclear fuel. The results demonstrate that, in general, groundwater lifetime expectancy increases with depth and it is greatest inside major matrix blocks. Various sources and aspects of uncertainty are considered, specifically geometric and hydraulic parameters of permeable fracture zones. Sensitivity analyses indicate that the existence and location of permeable fracture zones and the relationship between fracture zone permeability and depth from ground surface are the most significant factors for lifetime expectancy distribution in such a crystalline rock environment. As a consequence, it is successfully demonstrated that the concept of lifetime expectancy can be applied to siting and performance assessment studies for deep geologic repositories in crystalline fractured rock settings.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".