Temperate Coastal Microbial Communities Rapidly Respond to Low Concentrations of Partially Weathered Diesel
Bibliographic record
Abstract
Diesel is frequently encountered in coastal ecosystems due to land run-off from road surfaces. The current study investigates how partially weathered diesel at environmentally relevant concentrations, as may be seen during a run-off event, affect coastal microbial communities. A mesocosm experiment using seawater from the Bedford Basin, Nova Scotia, was followed for 72 h after the addition of partially weathered diesel. Sequencing data suggests partially weathered diesel acts quickly to alter the prokaryotic community, as both opportunistic (Vibrio and Lentibacter) and oil-degrading (Colwellia, Sulfitobacter, and Pseudoalteromonas) bacteria proliferated after 24 h in comparison to the control. In addition, total prokaryotes seemed to recover in abundance after 24 h, where eukaryotes only ceased to decrease slightly at 72 h, likely because of an inability to adapt to the oil-laden conditions, unlike the prokaryotes. Considering there were no highly volatile components (benzene, toluene, ethylbenzene, and xylene) present in the diesel when the communities were exposed, the results indicate that even a relatively small concentration of diesel run-off can cause a drastic change to the microbial community under low energy conditions. Higher energy conditions due to wave action may mitigate the response of the microbial communities by dilution and additional weathering of the diesel.
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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".