Opsin expression predicts male nuptial color in threespine stickleback
Bibliographic record
Abstract
Theoretical models of sexual selection suggest that male courtship signals can evolve through the build-up of genetic correlations between the male signal and female preference. When preference is mediated via increased sensitivity of the signal characteristics, correlations between male signal and perception/sensitivity are expected. When signal expression is limited to males, we would expect to find signal-sensitivity correlations in males. Here, we document such a correlation within a breeding population of threespine stickleback mediated by differences in opsin expression. Males with redder nuptial coloration express more long-wavelength-sensitive (LWS) opsin, making them more sensitive to orange and red. This correlation is not an artifact of shared tuning to the optical microhabitat. Such correlations are an essential feature of many models of sexual selection, and our results highlight the potential importance of opsin expression variation as a substrate for signal-preference evolution. Finally, these results suggest a potential sensory mechanism that could drive negative frequency-dependent selection via male-male competition and thus maintain variation in male nuptial color.
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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 | 0.000 | 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".