Evaluation of Arsenic Leaching Potential in Gold Mine Tailings Amended with Peat and Mine Drainage Treatment Sludge
Bibliographic record
Abstract
Peat and mine drainage treatment sludge can be valorized as amendments on mine sites to stabilize gold mine tailings and reduce the potential leaching of contaminants in pore water. However, the influence of organic amendments on the mobility of metalloids and/or metals in the tailings must be validated, as the leached contaminants may vary according to their type, nature, and origin. The objective of the present study was to evaluate over time the effect of peat‐ and/or Fe‐rich sludge amendments on the mobility of As and metallic cations in the drainage water of tailings potentially producing contaminated neutral drainage. Ten duplicated weathering cell experiments containing tailings alone or amended with peat and/or Fe‐rich sludge (5–10% dry weight) were performed and monitored for 112 d. The results showed that as low as 5% peat amendment would promote As mobility in tailings’ pore water, with As concentrations exceeding Quebec discharge criteria (>0.2 mg L −1 ). In addition, As(III), the most mobile and toxic form, was predominant with 10% peat, whereas organic species were negligible in all cells. The use of peat alone as organic amendment for the stabilization of tailing contaminants could increase the risk of generating As‐rich contaminated neutral drainage. Conversely, the mix of only 5% Fe‐rich sludge with or without peat decreased As concentrations in leachates by 65 to 80%. Further studies on the use of “peat” or “peat + Fe‐rich sludge” as cover or amendment should be conducted with a focus on Fe/As and Ca/As ratios. Core Ideas Peat amendments enhanced the leaching of As from gold mine tailings. Amendments of 5% peat promoted As(V) leaching, whereas 10% peat increased As(III) leaching. As(III) was predominant at ≥20 mg L −1 dissolved organic C from peat. Mine drainage treatment sludge could decrease As concentrations by 65 to 80% in tailings’ pore water.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 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 | 0.000 | 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 teacher head, 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".