Shear and dewatering behaviour of densified gold tailings in a laboratory simulation of multi-layer deposition
Bibliographic record
Abstract
Tailings may undergo desiccation stress history under varied climatic and depositional parameters. While tailings substantially dewatered prior to deposition may experience desiccation under the greatest range of climatic variation, even conventionally deposited tailings may desiccate in arid climates at lower rates of rise. Bench-scale research has shown that the stress history imparted by desiccation substantially improves strength in gold tailings. The present study further investigates this phenomenon by simulating multi-layer deposition of high-density tailings using a modular drying box, 0.7 m by 1 m in plan. The box is instrumented for directly measuring evaporation, drainage, water content, vertical volume change, and matric suction. Additional measurements included total suction at the surface as well as observations of crack development. The dewatering behaviour conforms to that predicted by previously published generic modelling, specifically that the presence of partially desiccated tailings initially accelerates, but then decelerates dewatering of fresh tailings. The shear behaviour of samples obtained using buried tubes and by driving thin-wall tubes into the multi-layer simulation are compared with shear behaviour of samples from bench-scale experiments. Shear strength of samples from the multi-layer simulation is independent of the sampling method, and shows higher strength than the bench-scale samples. The higher strength may be due to the greater number of wet–dry cycles or other age-related processes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".