Changes in Temperature Alter the Toxicity of Polycyclic Aromatic Compounds to American Lobster (<i>Homarus americanus</i>) Larvae
Bibliographic record
Abstract
Polycyclic aromatic compounds (PACs) present in the water column are considered to be one of the primary contaminant groups contributing to the toxicity of a crude oil spill. Because crude oil is a complex mixture composed of thousands of different compounds, oil spill models rely on quantitative structure-activity relationships like the target lipid model to predict the effects of crude oil exposure on aquatic life. These models rely on input provided by single species toxicity studies, which remain insufficient. Although the toxicity of select PACs has been well studied, there is little data available for many, including transformation products such as oxidized hydrocarbons. In addition, the effect of environmental influencing factors such as temperature on PAC toxicity is a wide data gap. In response to these needs, in the present study, Stage I lobster larvae were exposed to six different understudied PACs (naphthalene, fluorenone, methylnaphthalene, phenanthrene, dibenzothiophene, and fluoranthene) at three different relevant temperatures (10, 15, and 20 °C) all within the biological norms for the species during summer when larval releases occur. Lobster larvae were assessed for immobilization as a sublethal effect and mortality following 3, 6, 12, 24, and 48 h of exposure. Higher temperatures increased the rate at which immobilization and mortality were observed for each of the compounds tested and also altered the predicted critical target lipid body burden, incipient median lethal concentration, and elimination rate. Our results demonstrate that temperature has an important influence on PAC toxicity for this species and provides critical data for oil spill modeling. More studies are needed so oil spill models can be appropriately calibrated and to improve their predictive ability. Environ Toxicol Chem 2023;42:2389-2399. © 2023 SETAC.
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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".