The Influence of Operational Sex Ratio on the Intensity of Competition for Mates
Bibliographic record
Abstract
The evolution and maintenance of secondary sexual characteristics and behavior are heavily influenced by the variance in mating success among individuals in a population. The operational sex ratio (OSR) is often used as a predictor of the intensity of competition for mates, as it describes the relative number of males and females who are ready to mate. We investigate changes in aggression, courtship, mate guarding, and sperm release as a function of changes in the OSR using meta-analytic techniques. As the OSR becomes increasingly biased, aggression increases as competitors attempt to defend mates, but this aggression begins to decrease at an OSR of 1.99, presumably due to the increased costs of competition as rivals become more numerous. Sperm release follows a similar but not significant trend. By contrast, courtship rate decreases as the OSR becomes increasingly biased, whereas mate guarding and copulation duration increase. Overall, predictable behavioral changes occur in response to OSR, although the nature of the change is dependent on the type of mating behavior. These results suggest considerable flexibility of mating system structure within species, which can be predicted by OSR and likely results in variation in the strength of 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".