A pragmatic approach to abstract the excavation damaged zone around tunnels of a geological radioactive waste repository: application to the HG-A experiment in Mont Terri
Bibliographic record
Abstract
Abstract The excavation damaged zone (EDZ) around the backfilled tunnels of a geological repository represents a possible release path for radionuclides, corrosion and degradation gases that needs to be adequately addressed by safety assessment (SA) modelling tools. The hydromechanical phenomena associated with the creation and temporal evolution of the EDZ are of high complexity, precluding detailed representations of the EDZ in conventional SA. Thus, simplified EDZ models mimicking the safety-relevant features of the EDZ are required. In this context, a heuristic modelling approach has been developed to represent the creation and evolution of the EDZ in an abstracted and simplified manner. The key features addressed are the stochastic character of the excavation-induced fracture network and the self-sealing processes associated with the re-saturation after backfilling of the tunnels. The approach has been applied to a range of generic repository settings to investigate the impact of repository depth and in situ conditions on the hydraulic significance of the EDZ after repository closure. The model has been benchmarked with a dataset from a self-sealing experiment at the Mont Terri underground rock laboratory (URL), demonstrating the ability of the approach to mimic the evolution of the hydraulic significance of the EDZ during the re-saturation phase.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".