Microbial Communities Involved in Methane Production from Hydrocarbons in Oil Sands Tailings
Bibliographic record
Abstract
Microbial metabolism of residual hydrocarbons, primarily short-chain n-alkanes and certain monoaromatic hydrocarbons, in oil sands tailings ponds produces large volumes of CH(4) in situ. We characterized the microbial communities involved in methanogenic biodegradation of whole naphtha (a bitumen extraction solvent) and its short-chain n-alkane (C(6)-C(10)) and BTEX (benzene, toluene, ethylbenzene, and xylenes) components using primary enrichment cultures derived from oil sands tailings. Clone libraries of bacterial 16S rRNA genes amplified from these enrichments showed increased proportions of two orders of Bacteria: Clostridiales and Syntrophobacterales, with Desulfotomaculum and Syntrophus/Smithella as the closest named relatives, respectively. In parallel archaeal clone libraries, sequences affiliated with cultivated acetoclastic methanogens (Methanosaetaceae) were enriched in cultures amended with n-alkanes, whereas hydrogenotrophic methanogens (Methanomicrobiales) were enriched with BTEX. Naphtha-amended cultures harbored a blend of these two archaeal communities. The results imply syntrophic oxidation of hydrocarbons in oil sands tailings, with the activities of different carbon flow pathways to CH(4) being influenced by the primary hydrocarbon substrate. These results have implications for predicting greenhouse gas emissions from oil sands tailings repositories.
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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 | 0.000 |
| Science and technology studies | 0.000 | 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".