Instability of gold mine tailings subjected to undrained and drained unloading stress paths
Bibliographic record
Abstract
The drained instability of two gold mine tailings under lateral stress relief is investigated in this study. Constant deviator stress (CDS) unloading tests were performed using a triaxial apparatus to examine instability imposed by unloading in a drained condition. Instability was induced by enforcing a constant deviator stress while simultaneously reducing the mean effective stress. Stress paths and shear strengths of CDS tests were compared with undrained triaxial tests on comparable specimens which were anisotropically consolidated to the same initial stress ratios of the CDS tests. Several techniques were employed to determine the onset of instability in the CDS tests. The effects of unloading rate and initial stress ratio on the behaviour and the triggering of instability in the CDS tests were further investigated. It was observed that specimens consolidated to the same void ratio and initial stress ratio undergo instability at similar stress ratios or friction angles in both CDS and undrained shear tests. This suggests that the instability characteristic of tailings prone to stress relief can be predicted using undrained triaxial tests on anisotropically consolidated specimens. Critical states and state parameters of specimens subjected to CDS and unloading stress paths were also compared and analysed.
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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 | 0.001 |
| 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".