Role of Microbial Activity in Fe and S Cycling in Sub-Oxic to Anoxic Sulfide-Rich Mine Tailings
Bibliographic record
Abstract
Acid-mine drainage is an important environmental problem associated with base-metal mining and numerous studies have looked at the chemical and microbial processes involved in the oxidation of metal sulfides under oxic conditions. However, little is known about the activity of bacteria living in the same sulfide-rich tailings, but under sub-oxic to anoxic conditions. Recent work on Cu-Zn mine tailings have shown that sulfate-reducing bacteria (SRB) are present and active in these tailings (under a wide range of pH conditions) and that their activity is season dependent. In fact, acidic conditions and low organic carbon availability in the spring tend to greatly limit the activity of SRB in the tailings. On the other hand, IRB populations tend to increase in number in the spring, maybe as a result of the lack of activity of SRB. These findings are in agreement with studies on acidic coal mining lakes in Germany, which showed that IRB were mainly active in acidic and oxic sediments whereas SRB dominated in the more anoxic and pH neutral sediments. The presence and activity of SRB and IRB also represents a potential bioremediation tool, because both microbial pathways generate alkalinity. Recent work on acidic mining lakes indicates that IRB and SRB activity could be enhanced in a control manner and be used as an acidity neutralizing process to treat environments impacted by AMD.
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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 | 0.001 | 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".