Quantifying the potential for sexual dimorphism using upper limits on Bateman gradients
Bibliographic record
Abstract
Sex differences in the correlation between number of offspring and number of mates likely drive much of the evolution of morphological and behavioral sexual differences. These correlations have traditionally been represented by slopes from regressions of number of offspring on number of mates (Bateman gradients). Typically the Bateman gradient is assumed to be large for males and zero for females. However, five of nine examples where male and female gradients have been measured show appreciable gradients for females. Difference in these 'actual' gradients reflect sex differences in the force driving sexual selection. In the lab it is simple to estimate the 'upper limits' on these Bateman gradients. Differences between male and female upper limits can be used to quantify the potential for sexual dimorphism. We demonstrate how to estimate these upper limits in a katydid (Conocephalus nigropleurum) where males provide females with a large food gift (nuptial gift) during mating. By mating males and females to either one or two virgin mates, we estimated the way maximum fecundity increased with additional mates for each sex, giving an estimate of the upper limit of sexual selection on each sex. We compared these estimates to predictions based on the relative value of the nuptial gift and female pre-mating fecundity. Contrary to expectation, the male upper limit did not exceed the female upper limit. Both the fact that a male's second nuptial gift was smaller than his first and that many matings failed to transfer appreciable numbers of sperm seem to have contributed to the unexpected result.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".