Improving Tailings Dam Safety via Soil Treatment
Bibliographic record
Abstract
Mine tailings are the byproduct of mining activities, which need to be disposed of once the minerals in the ore are extracted. They can be disposed of in either dry or wet forms. The latter is most common, with the tailings being disposed of in the form of slurry inside retention structures. The retention structure may be a natural or manmade dam, with a predominant use of the upstream method due to its cost-effectiveness. This study analyzes the stability of an upstream tailings dam considering its staged construction. A two-dimensional nonlinear finite element model was developed using the program Plaxis 2-D to investigate the potential for stabilizing the tailings dam by using emulsified polymer and a mixture composed of cement kiln dust (CKD) and re-cycled gypsum (B). The numerical model demonstrated that utilizing a CKD: B mix increased the overall stability of the tailings impoundment above the conventional 1.5 safety factor requirements and indicated its usefulness in constructing robust dams whilst still being environmentally friendly.
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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".