Diverse sulfate- and sulfur intermediate- reducing bacteria contributing to annually expanding water anoxia in an oil sands pit lake
Bibliographic record
Abstract
Acidification and dissolved oxygen (DO) depletion in mining-affected water bodies are global environmental challenges associated with the oxidation of reduced S species (S reduced ), including sulfide and sulfur oxidation intermediate compounds (SOI). The recent detection of diverse SOI species in tailings impoundments and pit lakes highlights overlooked risks from microbial S cycling, that are difficult to geochemically quantify due to rapid S reduced turnover. This study characterizes sulfur-reducing bacteria (SRB; capable of sulfate and/or SOI reduction), in Base Mine Lake (BML), the first oil sands tailings reclamation end pit lake. Integrating field physicochemical, geochemical, 16S rRNA sequencing, and metagenomic results, we identified seasonal and DO-dependent variations in SRB communities that were associated with the spring-summer physicochemical and [S reduced ] changes. Specifically, late summer stratified BML water column evidenced DO-dependent SRB zonation, initiating with strictly anaerobic sulfate-reducing bacteria in bottom anoxic waters, and SRB SOI-reduction/disproportionation processes extending S impacts into the suboxic regions. The identification of SRB communities with diverse S reduction/disproportionation capabilities reveal the escalating environmental implications of annually recurrent and expanding sulfur cycling to BML water DO persistence. Findings here inform future oil sands tailings reclamation regulatory assessment, and provide essential insights for S biogeochemical cycling and outcomes in mining-impacted water contexts. • Expanding S cycling emergent threat to oil sands pilot end pit lake oxygen levels. • Spring-summer physico- and geo- chemical trajectories drive SRB distributions. • Late summer anoxia triggers sulfate-reducing SRB expansion and sulfide production. • Interlinked S intermediate species cycling extends S impacts on water O 2 impairment.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 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".