Bibliographic record
Abstract
Thickened and paste tailings disposal can provide a viable means to conserve water and reduce the risks associated with large volumes of water stored on the surface of tailings storage facilities. Thickened tailings deposition is a fast growing technology in the mining industry for both environmental and long term economical considerations. For surface deposition, the required beach slope and the tailings rheological properties often dictate the thickening process. The tailings beach slope is a key parameter in deposition requirements, which governs the storage capacity and the footprint of tailings management facilities. Prediction of the beach slope is fundamental for the design of non-segregating tailings stacks. But the existing tailings beach predictive models are not sufficiently developed as industrial standard applications. This paper presents a tailings deposition model and development of an analytical tool for prediction of tailings beach slopes. Dynamic profiles of tailings flows are also discussed. The mechanisms of tailings deposition and evolution of tailings beach slopes are investigated from both geotechnical and hydraulic engineering perspectives. The model is developed based on solid mechanics and fluid mechanics principles satisfying limit equilibrium conditions and energy conservation. The governing differential equations can be solved using a numerical technique and the solution scheme can readily be implemented in Fortran or other computer language. A design tool is developed using a Fortran code (BSLOPE), which takes into account rheological properties of tailings, operation parameters and site topographic conditions. The required properties of the tailings for beach slope predictions can be determined through laboratory testing and refined during operation. Case studies demonstrate that the modelling results are in agreement with field measurements. A sensitivity study was carried out to investigate the effects of the yield stress and discharge rate on the beach slope.
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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) | 0.001 | 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 | 0.001 | 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".