Factors influencing male affiliation and coalitions in a species with male dispersal and intense male–male competition, Colobus vellerosus
Bibliographic record
Abstract
MaleColobus vellerosuscompete intensely for access to females, which sometimes leads to mortal wounding. Yet, males often form cooperative relationships to overtake prime-aged males and immigrate into bisexual groups. We investigated the factors that predicted the presence of coalitions and affiliative relationships among males in this species. Interactions among males in 292 dyads from six groups were examined from 2004 to 2010 at Boabeng-Fiema, Ghana. Affiliation rates among males were higher and aggression rates lower when one or both males in the dyad were subadult, compared to adult male dyads. Affiliation rates tended to be higher among males that were kin but no other aspect of male relationships predicted affiliation. Coalitions among males were rarely observed and primarily occurred in the context of joint defense against extra-group males (93.5% of events). Adult males were more likely to provide coalitionary support than subadults and coalitions occurred significantly more often when both males were high ranking, since these males probably benefited most in terms of reproductive success from excluding extra-group males. Rank-changing and leveling coalitions among low-ranking males appear to be quite rare or absent inC. vellerosus. The costs of these types of coalitions may be too high or male group size too small on average for these types of coalitions to have been selected for. The overall low rates of affiliation and coalitions among maleC. vellerosusare likely influenced by male-biased dispersal and the high level of male–male competition.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".