Geographic and individual variation in carotenoid coloration in golden-crowned kinglets (Regulus satrapa)
Bibliographic record
Abstract
Phenotypic variation arises through natural selection, sexual selection, and genetic drift. Large-scale variation is often demonstrated via clinal gradients, and small-scale variation is commonly exhibited by quality-indicating traits. My goal was to investigate geographic and individual phenotypic variation in golden-crowned kinglets, with particular focus on carotenoid-based ornaments. Through a museum study, I found that kinglet body size and coloration weakly followed well-established ecogeographic rules. However, sexual dichromatism was reduced in colder climates, providing support for a poorly recognized environmentally-induced cline. In a separate study, I captured migrating kinglets to determine how carotenoid content mediates inter- and intrasexual crown colour variation. I found that crown coloration was associated with migration timing, and females displayed additional condition-dependence of this trait. Overall, small-scale variation in crown coloration is dependent on pigment differences and individual quality, while large-scale variation is likely governed by differences in selection pressures.
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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".