Workshop: Best-practice for laboratory testing low-permeable materials
Bibliographic record
Abstract
Abstract. The testing of low-permeable materials is challenging. Yet, for the disposal of radioactive waste, it is essential, too. This workshop is aimed at gathering ambitious scientists to discuss and to collaborate on their experiences in the laboratory testing of low-permeable materials. The focus here is on the methods: What method is best for what kind of low-permeable host rock (salt/clay) and for what kind of technical barrier material (bentonite/crushed salt)? How can measurement errors be correctly determined? What are the crucial “bottlenecks” in the device setups? How can high porous but low permeable samples best be pre-saturated? How can coupled flow and cumbersome gas traps in the tests be dealt with? What is the best-practice analysis of permeability from pressure decay recordings? Is there a hope of defining a standardized procedure for low-permeability testing? These points will be reflected in the light of radioactive waste disposal and in the need to find a best-practice solution when it comes to eventual evaluation and comparison of potential underground disposal sites.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.149 | 0.099 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.003 | 0.010 |
| Scholarly communication | 0.012 | 0.010 |
| Open science | 0.015 | 0.015 |
| Research integrity | 0.011 | 0.012 |
| Insufficient payload (model declined to judge) | 0.015 | 0.039 |
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".