Numerical study of the thm effects on the near-field safety of a hypothetical nuclear waste \nrepository - bmt1 of the decovalex iii project. part 1: conceptualization and characterization of \nthe problems and summary of results
Bibliographic record
Abstract
Geological disposal of the spent nuclear fuel uses often the concept of multiple barrier systems. In order to predict the performance of these barriers, mathematical models have been developed, verified and validated against analytical solutions, laboratory tests and field experiments within the international DECOVALEX III project. These models in general consider the full coupling of thermal (T), hydraulic (H) and mechanical (M) processes that would prevail in the geological media around the repository. For Bench Mark Test no. 1 (BMT1) of the DECOVALEX III project, seven multinational research teams studied the implications of coupled THM processes on the safety of a hypothetical nuclear waste repository at the near-fiel and are presented in three accompany papers in this issue. This paper is the first of the three companion papers, which provides the conceptualization and characterization of the BMT1 as well as some general conclusions based on the findings of the numerical studies. It also shows the process of building confidence in the mathematical models by calibration with a reference T-H-M experiment with realistic rock mass conditions and bentonite properties and measured outputs of thermal, hydraulic and mechanical variables.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".