Chemical dispersants enhance the activity of oil- and gas condensate-degrading marine bacteria
Bibliographic record
Abstract
Application of chemical dispersants to oil spills in the marine environment is a common practice to disperse oil into the water column and stimulate oil biodegradation by increasing its bioavailability to indigenous bacteria capable of naturally metabolizing hydrocarbons. In the context of a spill event, the biodegradation of crude oil and gas condensate off eastern Canada is an essential component of a response strategy. In laboratory experiments, we simulated conditions similar to an oil spill with and without the addition of chemical dispersant under both winter and summer conditions and evaluated the natural attenuation potential for hydrocarbons in near-surface sea water from the vicinity of crude oil and natural gas production facilities off eastern Canada. Chemical analyses were performed to determine hydrocarbon degradation rates, and metagenome binning combined with metatranscriptomics was used to reconstruct abundant bacterial genomes and estimate their oil degradation gene abundance and activity. Our results show important and rapid structural shifts in microbial populations in all three different oil production sites examined following exposure to oil, oil with dispersant and dispersant alone. We found that the addition of dispersant to crude oil enhanced oil degradation rates and favored the abundance and expression of oil-degrading genes from a Thalassolituus sp. (that is, metagenome bin) that harbors multiple alkane hydroxylase (alkB) gene copies. We propose that this member of the Oceanospirillales group would be an important oil degrader when oil spills are treated with dispersant.
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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) | 0.001 | 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".