Comparative toxicity of four chemically dispersed and undispersed crude oils to rainbow trout embryos
Bibliographic record
Abstract
The chronic toxicity of crude oil to fish embryos depends on the chemical constituents of the test oil and on factors that control the exposure of embryos to those constituents. The partitioning of chemicals from oil to water depends on the surface area of oil exposed to water and thus on the susceptibility of oil to be dispersed into droplets. The chronic toxicity of four different crude oils to embryos of rainbow trout (Oncorhynchus mykiss) was measured by exposure to the water-accommodated fraction (WAF; no droplet formation) and to the chemically enhanced WAF (CEWAF) of each oil. When effects were compared with the amount of WAF or CEWAF added to test solutions, chemical dispersion increased toxicity dramatically, by >35 to >300-fold, with the smallest difference measured for the lightest and least viscous oil. When effects were compared with measured concentrations of oil in test solutions, there were no differences in toxicity between WAF and CEWAF treatments, indicating that chemical dispersion promoted droplet formation and the partitioning of hydrocarbons from oil to water. On a dilution basis, the differences in toxicity among the four oils were correlated with the concentrations in oil of polynuclear aromatic hydrocarbons (PAH), particularly those with three to five rings, and with their viscosity, an index of dispersibility. However, when PAH concentrations were measured in solution, toxicity did not vary substantially among the four oils, suggesting that the PAH of each oil had equivalent toxicities and that differences in toxicity represented differences in dispersability.
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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".