Indicators of Microbial Sulfate Reduction in Acidic Sulfide-Rich Mine Tailings
Bibliographic record
Abstract
Sulfate-reducing bacteria (SRB) are thought to be actively involved in the cycling of sulfur in acidic mine tailings. However, most studies have used circumstantial evidence to assess microbial sulfate activity in such environments. In order to fully ascertain the role of sulfate-reducing bacteria (SRB) in sulfur cycling in acidic mine tailings, we measured sulfate reduction rates, sulfur isotopic composition of reduced sulfide fractions, porewaters and solid-phase geochemistry and SRB populations in four different Cu-Zn tailings located in Timmins, Ontario, Canada. The tailings were sampled in the summer and in the spring, shortly after snowmelt. The results first indicate that all four sites showed very high sulfate reduction rates in the summer (∼100–1000 nmol cm− 3d−1), which corresponded to the presence of sulfide in the porewaters and to high SRB populations. In some of the sites, zones of microbial sulfate reduction also corresponded to a decline of organic carbon and to an apparent pyrite (with slightly negative δ34S values) enrichment around the same depth. Microbial sulfate reduction was also important in permanently acidic (pH 2–3) mine tailings sites, suggesting that SRB can be active under very acidic conditions. Secondly, the results showed that microbial sulfate reduction was greatly reduced in the spring, suggesting that temperature might be a key factor in the activity of SRB. However, a closer look at the results indicated that temperature was not the sole factor and that acidic conditions and limited substrate availability in the spring appeared to be important as well in limiting microbial sulfate par reduction in sulfidic mine tailings. Finally, the results indicate that sulfur undergoes rapid cycling throughout the year and that microbial sulfate reduction and metal sulfide precipitation do not appear to be a permanent sink for metals.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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".