Female social experience affects the shape of sexual selection on males
Bibliographic record
Abstract
Background: An increasing number of factors have been shown to affect female mating behaviour, and thus to affect the strength and/or direction of selection that females exert on males. One of these factors is female social experience (including mating history). Question: How does female social experience affect the strength and direction of selection on four male traits – age, body size, weaponry size, and body condition? Methods: I used multivariate selection analysis to estimate the linear and non-linear selection gradients exerted by female field crickets (Gryllus pennsylvanicus) with different social backgrounds. Females were either virgins with no experience of conspecifics as adults or experienced females from a large, mixed-sex population. I assessed relative fitness through mating success (mated or not) and calculated selection gradients for the four male traits. Results: Experienced females exerted significant positive directional selection on male weaponry size and favoured older males. However, linear variation in these traits did not affect the probability of an inexperienced female mating. I also detected correlational selection by inexperienced females, who preferred combinations of age and body size (old/large and young/small).
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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 | 0.001 | 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 | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 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".