New equations for swelling characteristics of bentonite-based buffer materials
Bibliographic record
Abstract
Compacted bentonite and sandbentonite mixtures are attracting greater attention as buffer material for repositories of high-level nuclear waste. This buffer material is expected to fill up the space between the canisters containing the waste and the surrounding ground by swelling. To produce the specifications, such as dry density, sandbentonite mass ratio, and dimensions, of the buffer material, the swelling characteristics of compacted bentonite and sandbentonite mixtures must be evaluated quantitatively. New equations for evaluating the swelling behavior of compacted bentonite and sandbentonite mixtures are presented that can accommodate the influences of the sandbentonite mass ratio and the exchangeable-cation composition of bentonite. The new method for predicting swelling characteristics is presented by combining the new equations with the theoretical equations of the GouyChapman diffuse double layer theory and of the van der Waals force, which can evaluate the repulsive and attractive forces of montmorillonite mineral (i.e., the swelling clay mineral in bentonite). Furthermore, the applicability of the new prediction method has been confirmed by comparing the predicted results with laboratory test results on the swelling deformation and swelling pressure of compacted bentonites and sandbentonite mixtures.Key words: bentonite, diffuse double layer theory, van der Waals force, nuclear waste disposal, swelling deformation, swelling pressure.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".