Acute-to-chronic ratios of aquatic phototoxicity of polycyclic aromatic hydrocarbons and the prediction of chronic phototoxicity using the phototoxic target lipid model
Bibliographic record
Abstract
Polycyclic aromatic hydrocarbons (PAHs) are a group of organic compounds that can be toxic to aquatic life at environmentally relevant concentrations. The toxicity of some PAHs increases in the presence of ultraviolet radiation (UVR). The phototoxic target lipid model (PTLM) builds on the narcotic target lipid model and predicts acute phototoxicity by accounting for the light absorbed by PAHs. Given the lack of chronic data for PAHs exposed to UVR, an acute-to-chronic ratio (ACR) was proposed as a means for extending this model for predicting chronic toxicity. No information could be found on ACRs derived for phototoxic chemicals and therefore, this study had two objectives: (a) to generate acute and chronic toxicity data with and without exposure to UVR; and (b) to test the ability of the PTLM to predict chronic toxicity using an ACR. Acute and chronic bioassays were conducted on eight species, with each exposed to three PAHs and two light treatments: with and without UVR. Although there was a general trend of higher ACRs in UVR treatments, there was no statistical difference across all species in the ACR values for UVR treatments. Data generated herein were combined with data collated through a literature search to generate a global ACR of 11.6. Twenty of the 24 predicted phototoxic effect concentration affecting 10% of species (EC10) values calculated using the PTLM and ACR were within a factor of 10 of the observed phototoxic EC10 or lethal concentration affecting 5% of species values. This study demonstrates the capability of ACR to be applied in the context of phototoxicity and therefore proposes an unprecedented methodology to predict the chronic phototoxicity of PAHs.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".