Experimental and numerical evaluation of the potential reuse of waste rock as an evaporation barrier in engineered cover systems: effects of particle size
Bibliographic record
Abstract
One of the key challenges for the mining industry is the development of effective and durable management, closure, and reclamation strategies for acid mine drainage (AMD) generating tailings storage facilities (TSF). The elevated water table technique is particularly effective in preventing AMD under humid and temperate climate, as it reduces oxygen fluxes by maintaining the tailings saturated. A protection layer made of coarse-grained material (capillary break) is often needed to enhance water infiltration and reduce evaporation by reducing capillaries upward flow. A sustainable approach therefore consists in reusing waste rock to build this layer, reducing surface-stored mine waste and the environmental impact of borrow pits. However, the heterogeneity and reactivity of waste rock have so far limited their large-scale valorization in cover systems. The objective of this research was to evaluate the potential reuse of different size fractions of waste rock in an evaporation barrier. Laboratory column tests were carried out to evaluate the effectiveness of the cover made of waste rock to control water balance. Results indicated that waste rock containing fine fractions were less efficient in reducing evaporation than coarser fractions without fines for shallow water table. Numerical simulations were also carried out successfully to reproduce the effect of particle size on evaporation fluxes. This study underlines the importance of optimizing the particle size distribution in the design of evaporation barriers.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".