Consolidation behavior of various types of slurry tailings co-disposed with waste rock inclusions: a numerical study
Bibliographic record
Abstract
Abstract The co-disposition of mine tailings and waste rock in tailings storage facilities (TSFs) could contribute to increase the consolidation rate and decrease long-term settlement of tailings. Non-linear change of tailings properties during the filling process and interaction between tailings and waste rock inclusion (WRI) are critical to mechanical analysis but can, however, be complicated to simulate. The question of net volume gains or losses of tailings was also raised. In this study, fully coupled analysis which considered continuous variation of hydraulic conductivity and stiffness of tailings were performed to assess the evolution of consolidation of various tailing types in the presence of WRI. Effects of volume ratio of tailings over WRI on the net volume was investigated. Finally, effect of several practical aspects such as filling rates, and instantaneous filling assumption were considered. Results indicated that WRI could increase by 3.3 times the rate of consolidation of tailings. The zone of influence of WRI on tailings consolidation varied for each tailings. Using updated properties showed significant differences compared to models with constant values. The application of WRI can reduce volume available for the storage of tailings and net volumetric change due to settlement of the tailings with or without WRI could be estimated explicitly. Equations predicting evolution of net volume with the changes in the volume ratio of tailings and WRI were accordingly proposed. WRI effects was more pronounced with the increase of the filling rate. Finally, instantaneous filling assumption had little effect on the simulated rate of consolidation.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".