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 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 | 0.000 | 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".