O2 partitioning of sulfur oxidizing bacteria drives acidity and thiosulfate distributions in mining waters
Bibliographic record
Abstract
Abstract The acidification of water in mining areas is a global environmental issue primarily catalyzed by sulfur-oxidizing bacteria (SOB). Little is known about microbial sulfur cycling in circumneutral pH mine tailing impoundment waters. Here we investigate biological sulfur oxidation over four years in a mine tailings impoundment water cap, integrating aqueous sulfur geochemistry, genome-resolved metagenomics and metatranscriptomics. The microbial community is consistently dominated by neutrophilic, chemolithoautotrophic SOB (relative abundances of ~76% in 2015, ~55% in 2016/2017 and ~60% in 2018). Results reveal two SOB strategies alternately dominate across the four years, influencing acid generation and sulfur speciation. Under oxic conditions, novel Halothiobacillus drive lower pH conditions (as low as 4.3) and lower [S 2 O 3 2− ] via the complete Sox pathway coupled to O 2 . Under anoxic conditions, Thiobacillus spp. dominate in activity, via the incomplete Sox and rDSR pathways coupled to NO 3 − , resulting in higher [S 2 O 3 2− ] and no net significant acidity generation. This study provides genomic evidence explaining acidity generation and thiosulfate accumulation patterns in a circumneutral mine tailing impoundment and has significant environmental applications in preventing the discharge of sulfur compounds that can impact downstream environments. These insights illuminate opportunities for in situ biotreatment of reduced sulfur compounds and prediction of acidification events using gene-based monitoring and in situ RNA detection.
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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".