Faculty Opinions recommendation of Anaerobic hydrocarbon degradation in candidate phylum 'Atribacteria' (JS1) inferred from genomics.
Bibliographic record
Abstract
The hydrocarbon-enriched environments, such as oil reservoirs and oil sands tailings ponds, contain a broad diversity of uncultured microorganisms.Despite being one of the few prokaryotic lineages that is consistently detected in both production water from oil reservoirs and stable hydrocarbon-degrading enrichment cultures originated from oil reservoirs, the physiological and ecological roles of candidate phylum "Atribacteria" (OP9/JS1) are not known in deep subsurface environments.Here, we report the expanded metabolic capabilities of Atribacteria as inferred from genomic reconstructions.Seventeen newly assembled medium-to-high-quality metagenomic assembly genomes (MAGs) were obtained either from co-assembly of two metagenomes from an Alaska North Slope oil reservoir or from previous studies of metagenomes coming from different environments.These MAGs comprise three currently known genus-level lineages and four novel genus-level groups of OP9 and JS1, which expands the genomic coverage of the major lineages within the candidate phylum Atribacteria.Genes involved in anaerobic hydrocarbon degradation were found in seven MAGs associated with hydrocarbon-enriched environments, and suggest that some Atribacteria could ferment short-chain n-alkanes into fatty acid while conserving energy.This study expands predicted metabolic capabilities of Atribacteria (JS1) and suggests that they are mediating a key role in subsurface carbon cycling.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.209 | 0.073 |
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".