Data from: Asymmetry in cross-sex cross-trait genetic covariances and the evolvability of sexual dimorphism
Bibliographic record
Abstract
The evolution of sexual dimorphism is predicted to resolve conflict that can arise from divergent evolutionary interests between sexes, enabling each sex to reach its fitness optimum. However, most of the genome is shared between sexes, which can lead to a genetic constraint for dimorphism evolution. Most studies of intersexual genetic constraints have focused on the effect of genetic correlations, rmf, for single traits. However, multivariate studies of the B-matrix of intersexual genetic covariances suggest that sexual dimorphism may be more evolvable than inferred from rmf because of the potential for indirect responses to selection from correlated traits. To comprehensively address this question, we collected and re-analyzed published estimates of B using a recently developed approach to quantify the evolvability of sexual monomorphism and dimorphism. We find that across the traits and species we study, the evolvability of dimorphism is lower than that of monomorphism, but also that sexually concordant and antagonistic selection are almost equally capable of producing dimorphism. We also find that asymmetry in B would affect the response to selection more in females than in males. Our results show that sexual dimorphism is more evolvable than studies of rmf suggest and underscore that sexually antagonistic selection is not required for the evolution of sexual dimorphism.
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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 | 0.003 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.038 | 0.007 |
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".