Effects of structure and pore-water chemistry on critical state behavior of bauxite residue
Bibliographic record
Abstract
Differences in the fabric of reconstituted triaxial samples may increase the difficulty of achieving a unique critical state for some soils. Moreover, changes in the salt concentration of bauxite residue can result in non-unique critical state lines (CSLs). To evaluate the effects of fabric and pore-water chemistry on the critical state of bauxite residue, this paper compares the triaxial compression behavior of intact, slurry consolidated, and various forms of moist tamped samples, at a range of salt concentrations. The variations in fabric were also investigated using scanning electron microscopy and nuclear magnetic resonance (NMR) tests. The changes in pore-water chemistry were analyzed using X-ray methods and changes in salt concentration. The results showed that the particle agglomeration induced during reconstitution resulted in a more significant shift of the CSL than the decrease in salt concentration. Microimaging and shear behavior of samples showed that the slurry consolidation method may be the most suitable method for representing in situ behavior of clayey bauxite residue. NMR findings suggest that variations in water retained in the micropores of reconstituted samples stem from differences in microstructure. The implications of changes in salt concentration and sample microstructure on the design of clayey bauxite tailings storage facilities are discussed.
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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 | 0.000 | 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".