Assessing the Toxicity of Individual Aromatic Compounds and Mixtures to American Lobster (<i>Homarus americanus</i>) Larvae Using a Passive Dosing System
Bibliographic record
Abstract
Abstract Aquatic exposures to aromatic compounds (ACs) may be important contributors to biological effects of oil spills. The present study examined the acute toxicity of 11 ACs and 3 binary AC mixtures on stage 1 American lobster larvae using a passive dosing test design. The ACs investigated covered a range of classes and log octanol–water partition coefficient values (KOW; 2.5–5.5). Silicone O-rings were used to partition ACs into seawater and maintain stable exposures. Exposed lobster larvae were assessed for mobility and survival at 3, 6, 12, 24, 36, and 48 h. Fluorometry and gas chromatography-mass spectrometry measurements confirmed well-defined substance exposures. Expressing lethality in terms of chemical activities yielded values between 0.01 and 0.1, consistent with a baseline mode of action. Analysis of time-dependent median lethal/effect concentration (L/EC50) values were used to determine incipient values. An expected linear relationship between the incipient log L/EC50 and log KOW was fit to the empirical toxicity data to derive critical target lipid body burdens for immobilization and lethality endpoints. These values indicate that American lobster larvae fall on the sensitive end of the acute species sensitivity distribution. We used AC toxicity data to successfully predict toxicity of binary mixtures assuming additive toxicity. The observed time-dependent toxicity was inversely related to log KOW and occurred more quickly than reported previously. The results contribute to improving models for predicting oil spill impacts on American lobster larvae populations. Environ Toxicol Chem 2021;40:1379–1388. © 2021 The Authors. Environmental Toxicology and Chemistry published by Wiley Periodicals LLC on behalf of 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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".