Predicting Early Life Stage Mortality in Birds and Fishes from Exposure to Low-Potency Agonists of the Aryl Hydrocarbon Receptor: A Cross-Species Quantitative Adverse Outcome Pathway Approach
Bibliographic record
Abstract
Abstract Dioxin-like compounds (DLCs) cause early life stage mortality of vertebrates through activation of the aryl hydrocarbon receptor (AhR). A prior study developed a cross-species quantitative adverse outcome pathway (qAOP) which can predict full dose–response curves of early life stage mortality for any species of bird or fish exposed to DLCs using the species- and chemical-specific 50% effect concentration (EC50) from an in vitro AhR transactivation assay with COS-7 cells. However, calculating a reliable EC50 for input into this qAOP requires the maximal response of the concentration–response curve to be known, which is not always possible for low-potency agonists, such as some polychlorinated biphenyls (PCBs). To enable predictions for these low-potency agonists, the present study revised this qAOP to use the effect concentration threshold (ECThreshold) from the in vitro AhR transactivation assay as input. Significant linear relationships were demonstrated between ECThreshold and the dose to cause 0, 10, 50, or 100% mortality among early life stages of 3 species of birds and 7 species of fish for 4 DLCs: 2,3,7,8-tetrachlorodibenzo-p-dioxin, PCB 126, PCB 77, and PCB 105. These 4 linear relationships were combined to form the revised qAOP. This qAOP using the ECThreshold enables prediction of experimental dose–response curves for lower-potency agonists to within an order of magnitude on average, but the prior qAOP using EC50 predicts experimental dose–response curves for higher-potency agonists with greater accuracy. Environ Toxicol Chem 2020;39:2055–2064. © 2020 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".