Relative sensitivities among avian species to individual and mixtures of aryl hydrocarbon receptor–active compounds
Bibliographic record
Abstract
Dioxins and dioxin-like compounds (DLCs) are potent toxicants to most vertebrates. Sensitivities to DLCs vary among species. In the present study, the sensitivities of avian species (chicken [Gallus gallus], ring-necked pheasant [Phasianus colchicus], and Japanese quail [Coturnix japonica]) to some polychlorinated dibenzo-p-dioxins and polychlorinated dibenzofurans (PCDD/Fs) were determined by using species-specific, in vitro, transactivation assays based on a luciferase reporter gene under control of species-specific aryl hydrocarbon receptors. In ring-necked pheasant and Japanese quail, 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD) was not the most potent inducer of toxic effects. Especially for Japanese quail, the relative potency values of most of 9 PCDD/Fs tested were greater than for TCDD. The rank order of avian species sensitivities to DLCs was chicken > ring-necked pheasant > Japanese quail. Effects of binary mixtures of TCDD, 2,3,7,8-tetrachlorodibenzofuran, and 2,3,4,7,8-pentachlorodibenzofuran were strictly additive. Moreover, we also found that the primary DLCs that were responsible for most of the potency of the DLC mixtures can be deduced by using ordination in a multidimensional space defined by the avian species sensitivities. Overall, the relative potency and the species sensitivities of these chemicals could guide risk assessments to wild species when exposure to mixtures of DLCs in the environment.
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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 | 0.001 | 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".