Using red mud bauxite for the neutralization of acid mine tailings: a column leaching test
Bibliographic record
Abstract
Acid mine drainage (AMD) is an environmental problem produced when sulphides come in contact with an oxidant (± bacteria) and water, producing acid generation and metals leaching. One solution proposed is to use red mud bauxite (RMB), which is very alkaline, to neutralize oxidized acidic tailings. A column leaching test has been set up to evaluate major aspects of field constraints. First, a field investigation was conducted in which RMB was spread in aggregates before mixing with tailings. This setup has been reproduced in the laboratory and compared with a homogeneous mixture. The analyses of the water effluent do not show any important difference between the two mixtures. Second, some studies show that the addition of Cl brine to RMB helps to maintain the long-term neutralization potential. Brine addition increased the concentrations of Ca, Mg, Na, K, and Cu in drainage water. Columns were set up with 10% and 20% RMB to evaluate the effect of the quantity applied. Addition of greater than 20% RMB increases the leachate alkalinity and concentrations of Al, Cu, Pb, As, Fe, and SO42– in drainage waters. The addition of 10% RMB, however, significantly improves the quality of drainage water over a period of 125 days and results in concentrations and pH values within the ranges of those recommended by Directive 019 of the Ministère de l'environnement, Québec.Key words: acid mine drainage, red mud bauxite, tailings, environmental geochemistry, neutralization.
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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 | 0.000 | 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".