This is<i>not</i>déjà vu all over again: male guppy colour in a new experimental introduction
Bibliographic record
Abstract
We use an experimental introduction in nature to examine factors that influence parallel evolution. In 1996, 200 high-predation guppies (Poecilia reticulata) from the Yarra River were introduced into the Damier River, which previously lacked guppies. Eight years later, we quantified the colour of wild-caught guppies ('phenotypic' divergence) and lab-reared guppies ('genetic' divergence) from low- and high-predation environments in both rivers. Phenotypic and genetic divergence between predation environments within the Yarra was evident for black and for orange. Phenotypic divergence within the Damier was parallel to the Yarra for black but not for orange. Genetic divergence was absent between predation environments within the Damier, but was evident when comparing both Damier populations to their Yarra ancestors. The evolution of male colour thus depends on factors other than the simple contrast between 'high' and 'low' predation. We suggest that the parallel evolution of male signalling traits may sometimes first require the parallel evolution of female preferences.
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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 | 0.000 | 0.000 |
| 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) | 0.004 | 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".