SUSTAINABILITY AND TAILINGS MANAGEMENT IN THE MINING INDUSTRY: PASTE TECHNOLOGY
Bibliographic record
Abstract
In this study, the basic principles of paste technology which has been implemented successfully in the mining industry for both waste volume reduction and environmental impact reduction were investigated. In the past two decades, paste technology has progressed from a research-based mine backfill idea to a widely accepted and cost-effective fill system with the potential to totally change the approach tailings are disposed on the surface. Application of paste tailings for modern mines was shown experimentally by laboratory and field testing, focusing on intrinsic (e.g., tailings, cement and mix water properties) and extrinsic (e.g., in situ mixing, placement and curing conditions) parameters. Results indicate that use of paste tailings as backfilling and disposal in the mining industry as an innovative and environmentally sound system can guarantee operational continuity by reducing environmental (e.g., tailings dam failure, acidic water formation) and financial concerns over the mine’s life. Finally, paste technology enables mining operators to better manage their problematic tailings produced in the mineral processing plants.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".