Cascading effects of pre-adult survival on sexual selection
Bibliographic record
Abstract
Sexual selection influences broad-scale patterns of biodiversity. While a large body of research has investigated the effect of mate competition on sexual selection, less work has examined how pre-adult life history influences sexual selection. We used a mathematical framework to explore the influence of pre-adult survival on sexual selection. Our model suggests that pre-adult male mortality will affect the strength of sexual selection when a fixed number of adult males have an advantageous mate-acquisition trait. When a fixed number of males have an advantageous mate-acquisition trait, sexual selection is expected to increase when pre-adult mortality is relatively low. By contrast, if a fixed proportion (rather than number) of adult males have a mate-acquisition trait, pre-adult male mortality is not expected to affect the strength of sexual selection. Further, if the advantageous mating trait affects pre-adult survival, natural and sexual selection can interact to influence the overall selection on the mating trait. Given that pre-adult mortality is often shaped by natural selection, our results highlight conditions under which natural selection can have cascading effects on sexual selection.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.006 | 0.001 |
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".