The effects of electron donors on the growth of sulfate-reducing bacteria in copper-zinc and gold mine tailings from Timmins, Ontario
Bibliographic record
Abstract
Previous studies have shown that sulfate-reducing bacteria (SRB) are present and possibly active in gold and copper-zinc mine tailings. Sulfate-reducing bacteria can play an important role in the geochemistry of the mine tailings as they are responsible for the precipitation of diagenetic iron monosulfides and pyrite, a potential source for the generation of acid mine drainage. On the other hand, the formation of iron monosulfides can also serve to immobilize trace metals, and therefore has potential benefits to the tailing water systems. In addition, microbial sulfate reduction generates alkalinity which can be used to neutralize some of the acidity generated by the oxidation of metal sulfides. To better understand the role that sulfate-reducing bacteria play on the geochemistry of mine tailings, this present study was designed to identify some of the factors controlling the growth of sulfate-reducing bacteria in the tailings. The main goal was to determine the influence of organic electron donors (specifically lactate, acetate, formate and pyruvate) on microbial sulfate reduction in closed batch systems possessing physico-chemical conditions (pH, redox potential) matching the in situ conditions of the tailings in order to identify the preferred electron donor. (Abstract shortened by UMI.)
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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 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".