DIFFERENTIAL SELECTION BETWEEN THE SEXES AND SELECTION FOR SEX
Bibliographic record
Abstract
Anisogamy is known to generate an important cost for sexual reproduction (the famous "twofold cost of sex"). However, male-female differences may have other consequences on the evolution of sex, due to the fact that selective pressures may differ among the sexes. On the one hand, intralocus sexual conflict should favor asexual females, which can fix female-beneficial, male-detrimental alleles. On the other hand, it has been suggested repeatedly that sexual selection among males may help to purge the mutation load, providing an advantage to sexual females. However, no analytical model has computed the strength of selection acting on a modifier gene affecting the frequency of sexual reproduction when selection differs between the sexes. In this article, we analyze a two-locus model using two approaches: a quasi-linkage-equilibrium (QLE) analysis and a local stability analysis, whose predictions are verified using a multilocus simulation. We find that costly sex can be maintained when selection is stronger in males than in females, but acts in the same direction in both. Complete asexuality, however, evolves under any other form of selection. Finally, we discuss how experimental measurements of fitness variances and covariances between sexes could be used to determine the overall direction and strength on selection for sex arising from differences in selection between males and females.
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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 | 0.002 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".