Sexual segregation in Eurasian wild sheep
Bibliographic record
Abstract
Sexual segregation, a widely observed phenomenon in vertebrates, is commonly categorized into habitat and social segregation. A universal explanation for sexual segregation is, however, lacking and debated. Causes of segregation and their division into proximate and ultimate causes is also highly debated, and the relative contributions of habitat and social factors to segregation are also seldom quantified. We studied the Eurasian wild sheep in trans-Himalayan rangelands to identify these causes and estimate the contributions of habitat and social components to sexual segregation. We observed male, lactating, and nonlactating female groups feeding during 3 springs and summers. The 3 groups strongly segregated and differed in their activity budgets, partly because of antipredation risks, with lactating females being the most vigilant (40% of the time). At the feeding habitat scale, males selected the patches of highest quality. We found that the social component contributed the most to segregation (70%) as compared with habitat segregation (30%). This is the first study that quantifies the contribution of habitat and social components to sexual segregation. We emphasize the role of antipredator behavior of lactating females on activity budgets and selection of relatively poor-quality sites as compared with other groups and illustrate that the usual division into proximal and ultimate causes of sexual segregation is not as clear-cut as usually presented.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".