Comparative genomic analysis, transcriptome and hydrocarbon-degrading capability of Vibrio sp. strain J502
Bibliographic record
Abstract
Marine oil spills occurring as a result of anthropogenic activity pose a threat to marine birds, mammals, fish, and human health due to the toxicity of the compounds present in crude oil. Petroleum hydrocarbons are one of the most widespread organic contaminants impacting marine ecosystem, and contamination of marine sediments or coastal environments by hydrocarbons presents a major threat and challenge for remediation. Hydrocarbons in the marine environment are degraded abiotically by wave action, wind, and currents, and biotically by marine microorganisms. The bacterial genera Vibrio, Alteromonas, Bacillus and Pseudomonas are examples of species that have been identified as having hydrocarbon degrading metabolic capabilities. The genus Vibrio is extremely diverse and can be found in a wide variety of environments. Several environmental isolates of Vibrio spp. have been identified as hydrocarbon-degraders. Due to their widespread presence and adaptability, it is worthwhile studying the characteristics and metabolic traits of locally isolated Vibrio to understand their potential interaction with crude oil that is introduced into the marine environment. Here, I investigate the genomic, phylogenetic, and biochemical properties of a hydrocarbon-degrading marine bacterium isolated in Logy Bay, Newfoundland. Additionally, I determine the transcriptomic profile of this bacterium in crude oil exposure conditions and conduct an experiment to determine the chemical composition of chemically dispersed crude oil over time when it is incubated with a culture of this bacterium.
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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 | 0.001 |
| 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) | 0.001 | 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".