FE-G: Gas dynamics over 10 years at the Full-Scale Emplacement experiment (Mont Terri, CH) 
Bibliographic record
Abstract
The long-term safety of deep geological repositories of radioactive waste can be affected by gas production, transport and consumption, i.e. via alteration of the chemical environment and/or gas pressure build up. The Full-scale Emplacement (FE) experiment recreates a 1:1 scale spent fuel (SF) emplacement drift constructed in the Underground Rock Laboratory at Mont Terri (Switzerland). FE is inside Opalinus Clay, with heaters simulating canisters and granular bentonite as buffer material. Since 2014, FE-G combines on- and off-site measurements of gases in the FE experiment, providing data to monitor and validate the prediction of gas safety-relevant processes. In this poster contribution we will present results from 10 years of gas monitoring, with particular focus on: The observed rapid loss of gaseous O2 after emplacement, with current understanding of (ir)reversible processes related to O2 from 3-D reactive transport model (COMSOL) investigations; The role of noble gases, in particular 4He, to describe the ongoing gas exchanges via diffusion (and advection) among the Opalinus Clay, the emplaced bentonite and the niche outside the emplaced tunnel. Acknowledgements Swisstopo and Mont Terri Consortium Bill Lanyon (Fracture Systems Ltd) for modelling support Companies supporting field and lab work (Entracers, Solexperts, Hydroisotop)
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".