Female preference for color-enhanced males: a test of the sensory bias model in medaka, a drab fish
Bibliographic record
Abstract
Abstract Sexual selection research has long focused on the evolution of female mate preferences. Most of the models that have been developed posit that mate preferences evolve in a mating context. In contrast, the sensory bias model proposes that mate choice preferences arise in a non-mating context, as a by-product of natural selection acting on a female’s perceptual system. Recent research has shown that many species of fishes, from across a large clade including poeciliids, goodeids, and medaka, have a bias for long wavelength (LW) colors (yellow, orange, red) in a non-mating context. Even species that do not have LW-colored ornaments, apparently because they have been lost secondarily, retain this latent bias for LW colors. Here, we predicted that female Oryzias latipes (Japanese medaka), a drab species with a latent preference for LW colors, would show a mate choice preference for males with an artificial secondary sexual trait—a colored stripe added to their flank. We confirmed that females were more responsive to red and orange objects in a non-mating context than to other colors. We also showed that females were less resistant towards males with an LW-colored stripe than to those enhanced with a non-LW stripe and that, for many females, responses towards specific LW colors were consistent across these non-mating and mating contexts. Therefore, our results provide support for the sensory bias model by providing a link between a sensory bias in a non-mating context and a mate choice preference in a drab species like medaka.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".