Eye for an eyespot: how iridescent plumage ocelli influence peacock mating success
Bibliographic record
Abstract
Each of the multicolored eyespots (ocelli) on the peacock’s (Pavo cristatus) train is a complex structure with a purple-black center surrounded by concentric blue-green and bronze-gold regions. To investigate the influence of all 3 of these colors on male mating success, we used a physiological model of peafowl vision to quantify those colors as females would perceive them during male courtship displays. Males display at about 45° to the right of the sun’s azimuth (on average) with the female directly in front, so we investigated how colors would be perceived when illuminated at 30°, 45°, and 60° to the right of a female observer. We studied 34 males displaying at leks in 3 feral populations and quantified their copulation success and the colors of their eyespots. Eyespot coloration explained half of the observed variation in peacock mating success, with the hue and iridescence of the blue-green patch being the most important color variables. When we experimentally masked ocelli on 9 males, their copulation success declined almost to 0, supporting the idea that the eyespots are a major focus of female attention and not a trait that is simply correlated with something else that influences female choice. Thus, our study shows that the blue-green eyespot color overwhelmingly influences peacock mating success. The influence of the other eyespot colors on male success is minimal at best, raising questions about their function.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".