Case study: the impact of tailings properties on conveying system designs
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Mine wastes, specifically tailings, are commonplace in mining operations and most mines dispose of their tailings wastes in wet impoundment structures. Consequently, the failure of wet impoundment structures is one of the most significant environmental liabilities for mining operations and recent failures have highlighted the perils of this type of tailings disposal strategy. Likewise, water scarcity continues to be a growing concern at many mines globally, specifically within arid regions. Risk mitigation priorities along with water resource conservations are steering the mining industry’s waste management of tailings away from wet impoundment and towards dewatered tailings and dry stack disposal. The handling of dewatered tailings is most efficiently performed with the operation of automatic conveyance systems and the deposition of the tailings achieved by mobile conveyor stacking systems. Lab testing and analysis have identified that mine waste tailings characteristics vary widely between mine samples due mostly to the ore’s mineral composition, particle size distribution, and moisture content. Evaluating the mine site’s tailings material samples for their conveyability and measuring their change in surcharge angle is the key to understanding how the tailings react at different moisture levels while being transported along the length of an overland conveyor. The results of the conveyability tests are used for the design and strategy for the material handling and waste disposal stacking systems. This paper will present case studies of multiple tailings samples, from various mine sites, at specifically determined moisture levels. During the conveyor simulation tests, the samples were measured and recorded for the initial angle of repose, surcharge angle, and material density. This paper aims to demonstrate that there are often significant differences between tailings samples’ physical and dynamic properties and how that relates to the parameters needed for accurate conveyor engineering design.
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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 | 0.001 |
| 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 it