Effect of technological voids on swelling behaviour of compacted bentonite–claystone mixture
Bibliographic record
Abstract
The effect of technological voids on the swelling pressure of compacted MX80 bentonite–Callovo-Oxfordian (COx) claystone mixture was investigated by simulating the technological voids with a pre-defined space between the compacted disks of the mixture. Both axial and radial swelling pressures were monitored. After the tests, the microstructure of samples at different positions was investigated using mercury intrusion porosimetry (MIP), together with the determination of dry density and water content. Results showed that two main processes, filling and homogenization, occurred during soil hydration. In the filling process, the initial technological voids were gradually filled and the axial swelling pressure tended to increase. In the homogenization process, the samples had a sealing zone and a swelling zone. The sealing zone was characterized by a lower dry density than the expected final dry density while the swelling zone was characterized by a larger one. From the MIP results, the sealing zone showed larger inaccessible-pore, medium-pore, and large-pore void ratios and a lower small-pore void ratio than the swelling zone. Over time, the medium and large pores in the sealing zone were compressed, while the small pores in the swelling zone decreased. The stabilized axial swelling pressure followed a unique relationship with the expected final dry density. Moreover, the swelling pressure anisotropy was found to decrease as the technological voids increased.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".