Mineral weathering and water chemistry of pyrrhotite- and serpentine-bearing mine wastes under freeze-thaw cycles
Bibliographic record
Abstract
The mineralogical composition of mine wastes is generally considered to be the most important determinant of mine water geochemistry. However, environmental factors can significantly modify the weathering behavior of these materials, and the failure to consider these factors in experimental work may yield biased results. This is a particularly important consideration for mines in arctic and sub-arctic regions, where environmental conditions are far from those of a typical laboratory. The present study focuses on evaluating the influence of ambient thermal conditions on the weathering of pyrrhotite- and serpentine-rich tailings. To this end, laboratory-scale leached columns were used to simulate weathering of tailings over 544 days either at room-temperature or under freeze-thaw cycles (−20 °C/+20.5 °C). Microbiological analyses performed as part of the initial material characterization were unable to detect microbial communities. Although this result was unexpected and unusual, we found that elemental sulfur, which is stable in the absence of sulfur-oxidizing bacteria, had accumulated in the weathered tailings, representing up to 89–92 % sulfur products from pyrrhotite oxidation. Thiosulfate and sulfate were important oxidation products in leachates under both thermal conditions; however, freeze-thaw cycling appeared to enhance the stability of thiosulfate. The dominance of elemental sulfur and thiosulfate, which are produced via non-acid-generating reactions, suggests that the hydrolysis of Fe 3+ and Al 3+ may have been comparatively important sources of acid generation. Serpentine dissolution was more extensive at room-temperature but likely provided little to no alkalinity under freeze-thaw conditions due to slower dissolution kinetics. Calcite and dolomite were the principal buffering phases despite their low abundance. The mobility of Zn seemed to not be limited in either test, whereas Fe and Ni were effectively sequestered via adsorption and/or coprecipitation with ferrihydrite and gibbsite. Under room-temperature conditions, depletion or passivation of the carbonate minerals gave rise to a new buffering regime controlled by gibbsite. Although this maintained the pH around 5.1, aqueous Fe and Ni concentrations spiked (max = 7.6 and 1.5 mM, respectively) as they were released or desorbed from the gibbsite. Leachate pH values remained slightly elevated in the freeze-thaw tests and had not yet stabilized by the end of the experiment; the sequestration of Fe and Ni was not disrupted.
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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".