Role of the iridescent eye in stickleback female mate choice
Bibliographic record
Abstract
Many vertebrates exhibit prominent body colours that are used in courtship and territorial communication. Some fishes also have an eye whose iris becomes iridescent during the mating season, as in the threespine stickleback. Behavioural studies in this species have focused on the redness of the throat/jaw as the primary determinant of female mate choice. Unlike the iridescent eye, however, the red throat/jaw is not present in all stickleback populations, suggesting that the colour of the eye may be equally important for female mate choice. Here, we used data on photoreceptors and environmental light to assess body conspicuousness and the colour contrast of courtship signals for stickleback populations living in a range of waters, from clear (mesotrophic) to red light shifted (dystrophic). This analysis indicated that the redness of the throat/jaw is expressed to enhance the contrast of the eye. To test the importance of eye colour as a courtship signal, we carried out mate choice experiments in which females were presented with identical videos of a courting male but for the colour of the eye and/or the throat/jaw. Females did not choose based on differences in throat/jaw redness between videos, but preferred males with the highest contrast between the eye and the throat/jaw. This result points to the blue iridescent eye as a primary courtship signal in stickleback female mate choice.
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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 | 0.000 | 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) | 0.002 | 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".