Sexual Dimorphism Affects Herd Composition in African Antelopes
Bibliographic record
Abstract
Herd behaviour is a crucial aspect of antelope development and survival. Understanding determinants of differences in herd composition is necessary to predict patterns of habitation, social interaction, and life history; thus, the possibility of sexual dimorphism acting as a dictator of herd size and composition in African antelope was explored. Sexual dimorphism is a key factor in determining intersexual and intrasexual interactions, which can create selection pressures driving divergence in behavioural and social traits. Consequently, it was predicted that sexually dimorphic species would exhibit increased social behaviour and be found in larger groups when compared to sexually monomorphic species. Two closely related species, sexually dimorphic impalas and non-sexually dimorphic hartebeests were compared using 60 camera trap photos obtained through the WildCam Gorongosa project to determine if there was a difference in herd size and the number of young observed. It was discovered that impalas were found in larger groups than hartebeests and that there was no difference in the proportion of offspring. This discrepancy can be attributed to sexually dimorphic females needing to group together for protection against predation as they lack horns for self-protection. Group size differences can imply tendencies to inhabit different habitats and exhibit unique social interactions within the herd while foraging. These findings are key in understanding herd behaviour in African antelopes and can be applied to accurately track and monitor antelope species’ success in relation to conservation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".