Numerical investigation of the influence of waste rock inclusions on tailings consolidation
Bibliographic record
Abstract
The construction of waste rock inclusions (WRIs) in a tailings impoundment constitutes an alternative disposal technique that aims at improving the management of mine wastes. This technique consists in strategically placing the WRIs inside the impoundment, at the beginning and during its operation, to form compartments (or cells) in which the tailings are stored. Such inclusions can serve various purposes. In this paper, the authors investigate the effects of drainage from WRIs on tailings consolidation during filling of the impoundment, using experimental data from hard rock mines that provide the hydrogeological and geotechnical properties introduced in the numerical analyses. The model used for the simulations represents a cross section of a portion of a tailings impoundment containing a WRI. The numerical calculations results illustrate how the WRI affects the dissipation of the excess pore water pressures (PWPs) during filling of the impoundment, and help quantify the extent of the zone of influence of the inclusion. The results from the parametric analysis show that the saturated hydraulic conductivity ksat, compression index Cc, thickness, and deposition rate of the tailings are the main factors that affect the efficiency of WRIs to accelerate dissipation of excess PWPs. The discussion that follows recalls the main limitations of this investigation and addresses practical aspects related to the WRIs technique.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".