Data from: Artificial selection on sexual aggression: correlated traits and possible trade‐offs
Bibliographic record
Abstract
Forced copulation is an extreme form of sexual aggression that can affect the evolution of sex-specific anatomy, morphology and behavior. To characterize mechanistic and evolutionary aspects of forced copulation, we artificially selected male fruit flies based on their ability to succeed in the naturally prevalent behavior of forced matings with newly eclosed (teneral) females. The low and high forced copulation lineages showed rapid divergence, with the high lineages ultimately showing twice the rates of forced copulation as the low lineages. While males from the high lineages spent more time aggressively pursuing and mounting teneral females, their behavior towards non-teneral and heterospecific females was similar to that of males from the low lineages. Males from the low and high lineages also showed similar levels of male-male aggression. This suggests little or no genetic correlations between sexual aggression and non-aggressive pursuit of females, and between male aggression towards females and males. Surprisingly however, males from the high lineages had twice as high mating success than males from the low lineages when allowed to compete for consensual mating with mature females. In further experiments, we found no evidence for trade-offs associated with high forced mating rates: males from the high lineages did not have lower longevity than males from the low lineages when housed with females, and four generations of relaxed selection did not lead to convergence in forced mating rates. Our data indicate complex interactions among forced copulation success and consensual mating behaviour, which we hope to clarify in future genomic work.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.120 | 0.034 |
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".