Toxicity of crude oil chemically dispersed in a wave tank to embryos of Atlantic herring (<i>Clupea harengus</i>)
Bibliographic record
Abstract
Tests of crude oil toxicity to fish are often chronic, exposing embryos from fertilization to hatch to oil solutions prepared using standard mixing procedures. However, during oil spills, fish are not often exposed for long periods and the dynamic nature of the ocean is not easily replicated in the lab. Our objective was to determine if brief exposures of Atlantic herring (Clupea harengus) embryos to dispersed oil prepared by standard mixing procedures was as toxic as oil dispersed in a more realistic model system. Embryos were first exposed to chemically dispersed Alaska North Slope crude and Arabian light crude oil for 2.4 h to 14 d from fertilization to determine if exposure time affected toxicity. Toxicity increased with exposure time, but 2.4-h exposures at realistic concentrations of oil induced blue-sac disease and reduced the percentage of normal embryos at hatch; there was little difference in toxicity between the two oils. Secondly, oil was chemically dispersed in a wave tank to determine if the resultant oil solutions were as toxic to herring embryos as laboratory-derived dispersed oil using a single exposure period of 24 h. Samples taken 15 min postdispersion were more toxic than laboratory-prepared solutions, but samples taken at 5, 30, and 60 min postdispersion were less toxic. Overall, the laboratory- and wave tank-derived solutions of dispersed oil provided similar estimates of toxicity despite differences in the methods for preparing test solutions, suggesting that laboratory and wave tank data are a reliable basis for ecological risk assessments of spilled oil.
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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".