Metagenomic evaluation of a Utah tar sand microbiota suggests the predominant hydrocarbonoclastic role of Actinobacteria
Bibliographic record
Abstract
Background: Occurrences of tar sands have been reported in 22 states in the United States; however, the largest deposit is located in southwestern Utah. It has been suggested that tar sands were created by the microbial degradation of immobile subsurface oil over several million years; however, little is known about the indigenous microbial communities in the bituminous tar sands. Methods: This study identified Utah tar sand microbiota using next-generation sequencing technology and characterized the functional diversity using community-level physiological profile (CLPP). Results: Microbiota identified in these tar sands are mainly affiliated with the Gram-positive Actinobacteria and representatives of genera that have also been previously shown to degrade aromatic hydrocarbons, including Arthrobacter , Dietzia , Janibacter , Nocardioides , Microbacterium , Agrococcus and Salinibacterium , suggesting that these microbes likely play roles in the biodegradation of oil-hydrocarbons. CLPP analysis revealed less than 24 h was needed for the first color development in the microplate wells containing the polymers, whereas the duration of the lag phase of the carboxylic acids was prolonged. Conclusions: The quick utilization of the polymers suggests that the indigenous microbial community, especially the actinomycetes in the tar sand habitat, are poised and primed to degrade these recalcitrant compounds.
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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 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".