Leading practice in tailings planning for high-throughput mining operations
Bibliographic record
Abstract
Tailings planning requires integration of managerial, geotechnical, hydrological, hydrogeological, mining, and process engineering disciplines. It is an important activity for all mineral processing operations but is critically important to high-throughput mining operations that produce large tailings volumes. The large volumes mean that deposition and water management conditions can change rapidly over short durations and lead to adverse production outcomes or failure. Both the Canadian oil sands and Chilean copper mining industries contain multiple examples of high-throughput mining operations. They provide useful case studies to identify leading practices in tailings planning and management. Copper and oil sands mining differ in their history, labor costs, tailings composition, regulatory requirements, risk tolerance, and climate. However, there remain opportunities for both industries to learn from each other. Different approaches to design, operation, water management and regulation provide opportunities to develop a leading practice approach to tailings planning. Examples of these practices include the separation of long and short-range planning activities, the use of probabilistic as opposed to deterministic mass balance models, a focus on the management of slimes (or fluid tailings) as a distinct tailings stream, and the integration of consolidation into volumetric tailings planning.
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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 | 0.009 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".