A model of the interaction between ‘good genes’ and direct benefits in courtship-feeding animals: when do males of high genetic quality invest less?
Bibliographic record
Abstract
Conflict between mates over the amount of parental investment by each partner is probably the rule except in rare cases of genetic monogamy. In systems with parental care, males may frequently benefit by providing smaller investments than are optimal for individual female partners. Females are therefore expected to choose males that will provide the largest amounts of parental investment. In some species, however, the preferred males provide less care than their rivals. Focusing on species in which males invest by feeding their mates, I use a simple model to demonstrate the conditions under which males preferred by females may have optimal donations that are smaller than those of less-preferred rivals. Pre-mating female choice may sufficiently bias the perception of mate availability of preferred males relative to their rivals such that preferred males gain by conserving resources for future matings. Similarly, 'cryptic' biases in favour of high-quality ejaculates by females can compensate for smaller than average donations received from preferred males. However, post-fertilization cryptic choice should not change the optimal donations of preferred males relative to their rivals. I discuss the implications of this work for understanding sexual selection in courtship-feeding animals, and the relevance of these systems to understanding patterns of investment for animals in general.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".