Geochemical behavior of amended and non-amended mine tailings as cover materials for acid mine drainage control: Column tests and reactive transport modeling
Bibliographic record
Abstract
Mining companies generate large volumes of waste rock and tailings every year. To reduce these volumes, mining companies can valorize them as construction materials for cover systems such as cover with capillary barrier effects (CCBE). However, questions remain related to the geochemistry of the leachate that percolates through CCBEs made from mining materials. Limestone amendment can be used for increasing the neutralizing potential (NP) of mining materials in the case where the materials have a risk to generate contaminants. This study aims at assessing the performance of low-sulfide tailings, amended or not, and non-acid generating waste rock as components of CCBEs. To do so, five column tests were conducted in the laboratory to assess the long-term geochemical evolution of waste-rock, low-reactive tailings (2 % pyrite), tailings amended with 8 wt% of limestone, CCBE with the moisture-retaining layer (MRL) made of low-reactive tailings (CCBE-T), and CCBE with the MRL made of amended tailings (CCBE-TA). The geochemical evolution of leachates from the different column tests was simulated with MIN3P, a multicomponent reactive transport model. The numerical model was calibrated using results from the column tests. Long-term simulations using the short-term calibrated models suggested that low-reactive tailings could produce AMD when exposed to laboratory conditions, while limestone amendments effectively neutralized the generated acidity and stabilized the pH. Furthermore, incorporating tailings as a MRL in a CCBE reduced sulfide oxidation in the long-term due to the high degree of saturation that limited oxygen diffusion and sulfide reactivity.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".