Estrous synchrony in a nonseasonal breeder: adaptive strategy or population process?
Bibliographic record
Abstract
The idea that female mammals can manipulate the duration of each other's estrus in an effort to influence the degree of synchrony between their periods of sexual receptivity is a persistent and popularly held one. It is frequently cited as proof of pheromonal communication in humans and often invoked by models of female reproductive strategies more generally. Yet, to date, no tests of the evolutionary arguments put forward by proponents of the phenomenon have been undertaken. We addressed this deficit with an analysis of the reproductive demography of wild female chacma baboons, where variance in the temporal distribution of female receptivity is known to occur. Specifically, we tested the predictions that this variance will reflect female attempts to minimize 1) the risks of being monopolized by a single male or 2) the intensity of interfemale competition for males. Using model comparison, we found no evidence that male number or operational sex ratio had any influence on the distribution of female receptivity, the number of females in estrus, or the duration of female sexual swellings. Indeed, when modeling estrous overlap and cycling female number, we found that a simple nondeterministic model provided the best fit. We conclude, therefore, that variance in the temporal distribution of female receptivity is indicative of nothing more than a population process and that socially mediated synchrony is not a tangible adaptive phenomenon.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".