Syntrophy of bacteria and archaea in the anaerobic catabolism of hydrocarbon contaminants
Bibliographic record
Abstract
The extensive use of organic chemicals has resulted in the widespread distribution of hydrocarbon contaminants (HCs) in many ecosystems on a global scale. Many subterranean ecosystems can rapidly become anaerobic or even methanogenic following hydrocarbon contamination. Bacteria and archaea dominate communities in such systems and mediate the syntrophic processes that transform HCs into methane (CH4). The resulting CH4 is oxidized by anaerobic bacteria and archaea, either jointly or individually, in the presence of electron acceptors (e.g., sulfate, nitrate, nitrite, manganese, or ferric iron), a process that reduces CH4 emissions and, as a result, contributes to climate change mitigation. Although the possibility of the syntrophy of bacteria and archaea in the anaerobic transformation of HCs and methane oxidation is widely established, the specific pathways and syntrophic taxa involved are poorly understood. This paper reviews the syntrophy of bacteria and archaea in anaerobic HC degradation, with a focus on methanogenic processes. In addition, we discuss the role of bacteria and archaea in the anaerobic oxidation of methane (AOM) and its environmental significance. Given that much of the biotransformation of HCs driven by methanogenic and methanotrophic processes remains unknown, we propose a way forward to discover novel syntrophic partners and metabolic pathways in such anoxic systems.
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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 | 0.001 | 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 | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".