Environmental selection influences the microbiome of subsurface petroleum reservoirs
Bibliographic record
Abstract
Abstract Petroleum reservoirs within the deep biosphere are extreme environments inhabited by diverse microbial communities creating biogeochemical hotspots in the subsurface. Despite their ecological and industrial importance, systematic studies of core microbial taxa and associated genomic attributes of the oil reservoir microbiome are limited. This study compiles and compares 343 16S rRNA gene amplicon libraries and 25 shotgun metagenomic libraries from oil reservoirs in different parts of the world. Taxonomic composition varies among reservoirs with different physicochemical characteristics, and with geographic distance. Despite oil reservoirs lacking a taxonomic core microbiome in these datasets, gene-centric metagenomic analysis reveals a functional core featuring carbon acquisition and energy conservation strategies consistent with other deep biosphere environments. Genes for anaerobic hydrocarbon degradation are observed in a subset of the samples and are therefore not considered to represent core biogeochemical functions in oil reservoirs. Metabolic redundancy within the petroleum reservoir microbiome reveals these to be deep biosphere systems poised to respond to changes in redox biogeochemistry. This highlights the potential to use microbial genomics for predicting microbial responses to (bio)engineering perturbations to these subsurface habitats.
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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 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".