Working the crowd: sociable vervets benefit by reducing exposure to risk
Bibliographic record
Abstract
Reduction of predation risk represents the most likely explanation for the evolution of group-living among the anthropoid primates. Obligate sociality leads to increased competition for resources, meaning that animals may face a trade-off between safe positions within the troop and increased foraging efficiency. Dominance has been proposed to be a major factor influencing spatial position within primate troops, but it is also possible that animals can improve their spatial position using social strategies, such as grooming. In many species, dominance rank and social preference (as expressed through grooming) are confounded. In our study population of vervet monkeys, however, dominance does not underpin social preference, enabling us to test whether 1) more sociable vervets experience reduced exposure to predation risk, as indexed by vigilance, and 2) that dominant animals accepted increased risk in order to forage at the front of the troop. We collected spatially explicit data on the individual locations of members of 2 troops at predetermined times over a 4-month period. We constructed bounded Voronoi tessellations for each temporal snapshot, with the area of each animal’s “tile” identifying its “domain of danger.” We also collected data on time spent vigilant and foraging, dominance rank, and grooming behavior. We found no effect of dominance, but animals with larger grooming networks were less exposed to predation risk, from which they benefitted through both reduced vigilance and increased foraging time. We interpret these results in the light of current debates about the ways in which sociality affects fitness.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".