Rapid dilution effectively decreases hydrocarbons following small oil spills, but impacts on microeukaryote communities are still observed
Bibliographic record
Abstract
Dilution affects the distribution and weathering of oil following a spill into coastal seawater, but including dilution in experiments is rare. In open water, dilution is expected to rapidly reduce the concentrations of hydrocarbons, and estimates of impacts and microbial responses from batch experiments may overestimate what happens during a spill event. Three experiments carried out in the southern Gulf of St. Lawrence incorporated periods of incubation of natural microbial communities with either Heidrun medium crude or Bunker C fuel oil followed by dilutions using unexposed seawater. Higher concentrations of n-alkanes (C10 – C35) in Heidrun, resulted in higher initial total hydrocarbon concentrations in incubations with Heidrun compared to Bunker C. Similar polycyclic aromatic hydrocarbon concentrations and potential toxicity were measured in the water column for both oils. Dilution effectively decreased the hydrocarbon concentrations, reducing them to background levels after two dilutions (13.8-fold). Neither the abundance nor the diversity of the prokaryote community was strongly affected by the addition of oil. The natural coastal microbiome was dominated by bacteria that may be facultative hydrocarbon degraders. The abundance of phytoplankton and the diversity of the microeukaryote community decreased during the second incubation in response to both Heidrun and Bunker C, potentially due to photooxidative byproducts produced under full sunlight. Dilution appears to limit the bacterial response to oil, but low hydrocarbon concentrations may still be linked to impacts on the microeukaryote community.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".