Characterization and genomic analysis of a diesel-degrading bacterium, Acinetobacter calcoaceticus CA16, isolated from Canadian soil
Bibliographic record
Abstract
Abstract Background With the high demand for diesel across the world, environmental decontamination from its improper usage, storage and accidental spills becomes necessary. One highly environmentally friendly and cost-effective decontamination method is to utilize diesel-degrading microbes as a means for bioremediation. Here, we present a newly isolated and identified strain of Acinetobacter calcoaceticus (‘CA16’) as a candidate for the bioremediation of diesel-contaminated areas. Results Acinetobacter calcoaceticus CA16 was able to survive and grow in minimal medium with diesel as the only source of carbon. We determined through metabolomics that A. calcoaceticus CA16 appears to be efficient at diesel degradation. Specifically, CA16 is able to degrade 82 to 92% of aliphatic alkane hydrocarbons (CnHn + 2; where n = 12–18) in 28 days. Several diesel-degrading genes (such as alkM and xcpR) that are present in other microbes were also found to be activated in CA16. Conclusions The results presented here suggest that Acinetobacter strain CA16 has good potential in the bioremediation of diesel-polluted environments.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 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".