Carbon and Sulfur Cycling by Microbial Communities in a Gypsum-Treated Oil Sands Tailings Pond
Bibliographic record
Abstract
Oil sands tailings ponds receive and store the solid and liquid waste from bitumen extraction and are managed to promote solids densification and water recycling. The ponds are highly stratified due to increasing solids content as a function of depth but can be impacted by tailings addition and removal and by convection due to microbial gas production. We characterized the microbial communities in relation to microbial activities as a function of depth in an active tailings pond routinely treated with gypsum (CaSO(4)·2H(2)O) to accelerate densification. Pyrosequencing of 16S rDNA gene sequences indicated that the aerobic surface layer, where the highest level of sulfate (6 mM) but no sulfide was detected, had a very different community profile than the rest of the pond. Deeper anaerobic layers were dominated by syntrophs (Pelotomaculum, Syntrophus, and Smithella spp.), sulfate- and sulfur-reducing bacteria (SRB, Desulfocapsa and Desulfurivibrio spp.), acetate- and H(2)-using methanogens, and a variety of other anaerobes that have been implicated in hydrocarbon utilization or iron and sulfur cycling. The SRB were most abundant from 10 to 14 mbs, bracketing the zone where the sulfate reduction rate was highest. Similarly, the most abundant methanogens and syntrophs identified as a function of depth closely mirrored the fluctuating methanogenesis rates. Methanogenesis was inhibited in laboratory incubations by nearly 50% when sulfate was supplied at pond-level concentrations suggesting that in situ sulfate reduction can substantially minimize methane emissions. Based on our data, we hypothesize that the emission of sulfide due to SRB activity in the gypsum treated pond is also limited due to its high solubility and oxidation in surface waters.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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 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".