Species traits predict the aryl hydrocarbon receptor 1 (AHR1) subtypes responsible for dioxin sensitivity in birds
Bibliographic record
Abstract
Differences in avian sensitivity to dioxin-like compounds (DLCs) are directly attributable to the identities of amino acids at two sites within the ligand binding domain (LBD) of the aryl hydrocarbon receptor 1 (AHR1). Recent work suggests that by influencing avian exposure to naturally occurring dioxins, differences in diet, habitat, and migration may have influenced the evolution of three AHR1 LBD genotypes in birds: type 1 (high sensitivity), type 2 (moderate sensitivity), and type 3 (low sensitivity). Using a boosted regression tree (BRT) analysis, we built on previous work by examining the relationship between a comprehensive set of 17 species traits, phylogeny, and the AHR1 LBD across 89 avian species. The 17 traits explained a combined 74% of the model deviance, while phylogenetic relatedness explained only 26%. The strongest predictors of AHR1 LBD were incubation period and habitat type. We found that type 3 birds tended to occupy aquatic habitats, and, uniquely, we also found that type 3 birds tended to have slower developmental rates. We speculate that this reflects higher evolutionary exposure to naturally occurring dioxins in waterbirds and species with K-selected life histories. This study highlights the value of trait-based approaches in helping to understand differing avian species sensitivities to environmental contaminants.
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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) | 0.000 | 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".