The effect of male age on patterns of sexual segregation in Siberian ibex
Bibliographic record
Abstract
Sexual segregation is very common in sexually size dimorphic ungulates and may be the result of different habitat preferences and/or differential social behaviours of males and females. Various hypotheses have been put forward to explain this phenomenon. In the present research, we examined sexual segregation in a quite poorly understood species, the Siberian ibex. The species presents a marked sexual size dimorphism, with adult males weighing double as much as females. We use the Sexual Segregation and Aggregation Statistics (SSAS) to analyze the sex-age patterns of sexual segregation in this species, to understand the relevance of social factors. Our results show that adult Siberian ibex males were socially segregated from females all year round, except during the rutting season. Furthermore, the degree of segregation between females and males was influenced by the age of males. Moreover, the patterns of social segregation within males also increased with male age, reaching maximum values for males of 9 years-old and older, which means male age plays an important role in the sexual segregation of this species. This study clearly shows that social factors play a key role in the sexual segregation of Siberian ibex.
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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".