Investigation of metal mobility in gold and silver mine tailings by single-step and sequential extractions
Bibliographic record
Abstract
Metal leachate from mine tailings has the potential to release toxic metals into the surrounding environment. A single-step extraction procedure mimicking rainwater and a three-step BCR sequential extraction procedure (acid, reducing and oxidizing conditions) were applied to gold (GMT) and silver (SMT) mine tailings. Major (Al, Ca, Fe, Mg, and Mn) and trace metals were monitored to better understand the mobility and geochemistry of these metals when exposed to various environmental leaching conditions. Rainwater extraction released only small quantities of metals, while the three-step BCR extraction was more effective in mobilizing metals from the tailings. Under the acidic conditions of BCR step 1, Ca, Mg, Cd, Cu, and Mn were released in high concentrations. The dissolution of Fe, Ca, and Mg were dominant along with Pb in step 2 (reducing conditions). In step 3 (oxidizing conditions), Fe was the most dominant species together with Co, Cu, Ni, and Se. A high fraction of Al, Be, Cr, Li, Mo, Sb, Tl, and V remained in the residue. From SMT, larger quantities of As, Ca, Cd, and Zn were released compared to GMT. The BCR extraction could be applied to tailings to predict the potential release of toxic metals from mine wastes; however, excessive amounts of Ca and Fe in the tailings could cause carry-overs and incomplete extraction and carry-overs, resulting in a misinterpretation of results.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".