Culturable bacterial pool from aged petroleum-contaminated soil: identification of oil-eating Bacillus strains
Bibliographic record
Abstract
Information gleaned from soil microbiota may provide access to new economically important species. Here, we describe the isolation, identification, and genetic diversity of high-density bacterial populations isolated from aged oil-contaminated soil. Twenty different morphotypes were identified in populations present at densities of up to 10 7 cells g −1 soil, encompassing seven bacterial genera based on 16S rRNA sequencing. Six isolates of the genus Bacillus were identified, three of which appear to consume oil. The genetic clusters defined by the DNA fingerprinting analysis suggest that there is a close relationship between these oil-eating Bacillus species. Isolates able to grow using crude oil as a carbon source were biochemically characterized and found to exhibit high lipolytic activity in liquid medium and to produce alkaline-stable biosurfactants. Fluorescence spectroscopy analysis of the cell-free extract from the oil-eating Bacillus sp. strain MO.04b showed an increase in the relative fluorescence intensity of low-molecular-mass aromatics concomitantly with an increase in the protein content, suggesting the transformation of aromatic hydrocarbons to the liquid phase in response to biodegradation. The approach adopted in this study suggests a low diversity of the high-density bacterial population colonizing an aged oil-contaminated soil and may prove useful in selecting bacterial isolates for bioremediation studies and biotechnological applications such as biosurfactant production.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".