Data from: Beauty in the eyes of the beholders: color vision is tuned to mate preference in the Trinidadian guppy (Poecilia reticulata)
Bibliographic record
Abstract
A broad range of animals use visual signals to assess potential mates, and the theory of sensory exploitation suggests variation in visual systems drives mate preference variation due to sensory bias. Trinidadian guppies (Poecilia reticulata), a classic system for studies of the evolution of female mate choice, provide a unique opportunity to test this theory by looking for co-variation in visual tuning, light environment, and mate preferences. Female preference co-evolves with male coloration, such that guppy females from ‘low predation’ environments have stronger preferences for males with more orange/red coloration than do females from ‘high predation’ environments. Here we show that color vision also varies across populations, with ‘low’ predation guppies investing more of their color vision to detect red/orange coloration. In independently colonized watersheds, guppies expressed higher levels of both LWS-1 and LWS-3 (the most abundant LWS opsins) in ‘low predation’ populations than ‘high predation’ populations at a time that corresponds to differences in cone cell abundance. We also observed that the frequency of a coding polymorphism differed between high and low predation populations. Together this shows that the variation underlying preference could be explained by simple changes in expression and coding of opsins, providing important candidate genes to investigate the genetic basis of variation in this model system.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.062 | 0.011 |
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".