Metagenomic and metatranscriptomic responses of natural oil degrading bacteria in the presence of dispersants
Bibliographic record
Abstract
Oil biodegradation has been extensively studied in the wake of the deepwater horizon spill, but the application of dispersant to oil spills in marine environments remains controversial. Here, we report metagenomic (MG) and metatranscriptomic (MT) data mining from microcosm experiments investigating the oil degrading potential of Canadian west and east coasts to estimate the gene abundance and activity of oil degrading bacteria in the presence of dispersant. We found that the addition of dispersant to crude oil mainly favours the abundance of Thalassolituus in the summer and Oleispira in the winter, two key natural oil degrading bacteria. We found a high abundance of genes related not only to n-alkane and aromatics degradation but also associated with transporters, two-component systems, bacterial motility, secretion systems and bacterial chemotaxis.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.002 | 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 teacher head, 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".