Safeguarding Geological Repositories – R&D Contributions from the German Support Programme
Bibliographic record
Abstract
Germany has contributed actively to R&D related to safeguarding geological repositories since 1980, first on the national level, since 1990 also through the SAGOR, SAGOR II and ASTOR group of experts, and other German Support Programme tasks. While earlier studies identified issues of essential relevance (aspects of retrievability, need for Design Information Verification (DIV), definition of physical boundaries) and developed safeguards approaches for geological repositories and encapsulation plants, R&D more recently has focused on technologies for safeguarding geological repositories. These are: i) Satellite imagery: In a joint task with the Support Programmes of Finland, Japan and Canada, the potential of synthetic aperture radar (SAR) data acquired by remote sensing satellites was demonstrated. ii) Geophysical methods: Measurement campaigns at the Gorleben exploratory mine showed the applicability of seismic and acoustic measurement methods to detect clandestine underground mining activities. Based on the results, a follow-up project has modelled the propagation of seismic waves from different sources in the salt and surrounding sediments. Another project has investigated the applicability of underground radar technology as a directive and wide ranging technology. Active radar systems could probably be used to set up a protective screen around a geological repository. iii) Autonomous navigation and localisation: The German Support Programme has recently started a feasibility study on simultaneous localisation and mapping (SLAM) systems for safeguards verification purposes. Results achieved during field tests have indicated several possibilities and challenges for using indoor navigation and mapping technologies in support of geological repositories safeguards. The paper presents the lessons learnt from the earlier studies and gives an overview of the results and implications of recent and on-going projects for safeguarding geological repositories.
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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 | 0.006 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.009 |
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".