Using Upper Limits of "Bateman Gradients" to Estimate the Opportunity for Sexual Selection
Bibliographic record
Abstract
The widespread use of molecular markers to estimate parentage makes possible a new index of the opportunity for sexual selection. After demonstrating the need for a new measure, I develop one based on the upper limit on sexual selection. I describe what sets the upper limit for each sex by showing how maximum fecundity increases with number of mates, accounting for the amount of energy (or critical resources) available for reproduction and levels of parental care. For females the upper limit on sexual selection is set by the value of paternal investment that comes with each mating. For males, the upper limit on sexual selection is set by the fecundity of their mates (including any boost to female fecundity from paternal investment). Sex-roles are most likely to reverse (making males choosy and females competitive) when the amount of reproductive energy investment made by each sex is low, irrespective of the level of paternal investment. Finally, I propose that we use the difference between male and female upper limits on sexual selection to quantify sex differences in the opportunity for sexual selection. Using upper limits to estimate the opportunity for sexual selection is more intuitive than older methods (e.g., standardized variance in mating success), it is experimentally measurable, and it is valuable in understanding the evolution of mating systems.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.010 |
| 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) | 0.002 | 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".