Commingling of Waste Rock and Tailings to Improve “Dry Stack” Performance: Design and Evaluation of Mixtures
Bibliographic record
Abstract
Mine tailings are typically deposited as slurry and stored in impoundment dams. These structures pose a serious geotechnical risk, and are difficult to successfully reclaim at the end of mining. An increasingly common alternative to traditional tailings disposal is “dry stacking”: the placement of tailings dewatered using filtration in a self-supporting stack. It has been demonstrated that the addition of rock (termed “commingling”) to a filtered tailings stack has the potential to improve the geotechnical performance of the stack and may make large-scale dry stacking more economically viable. This paper discusses the application of commingling to tailings dry stacking, specifically relating to the design and evaluation of commingled blends of waste rock and tailings. The authors present a review of existing mix design theory, and present an extended theoretical model to predict the structure and behavior of blends of waste rock and tailings, based upon mix ratio and density. This paper is based upon established theory, but is extended to consider the case of loosely placed materials and the effect of volume change on structural configuration. The extended model may be used to describe the geotechnical behavior of commingled filtered tailings and waste rock. It is postulated that the geotechnical behavior of blends, and the primary mechanism of volume change, is governed by particle configuration. A brief discussion of experimental methods to evaluate the structure and configuration of commingled mine wastes is also presented.
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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 | 0.001 | 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".