A test for the parallel co-evolution of male colour and female preference in Trinidadian guppies (Poecilia reticulata)
Bibliographic record
Abstract
Question: Do male traits and female preferences co-evolve in response to divergent natural selection? Organisms: Six Trinidadian guppy (Poecilia reticulata) populations adapted to high- or low-predation environments in three separate drainages. Methods: Measurement of colour patterns on wild-caught and lab-reared males. ‘No-choice’ mating experiments to quantify female preference functions for male traits. Comparisons of male colour and female preference functions between predation environments. Predictions: If divergent natural selection drives parallel co-evolution, both male traits and female preferences should be similar for populations in similar environments but different for populations in different environments. Conclusions: Male traits have broadly diverged in parallel between predation environments, leading to larger body size and increased colour in low-predation sites. Female preferences also appear to be diverging because females discriminate against colourful males in high-predation sites but not in low-predation sites. Despite this general pattern, deviations from parallel co-evolution were also present, suggesting a substantial role for other selective agents.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".