Individual-level movement bias leads to the formation of higher-order social structure in a mobile group of baboons
Bibliographic record
Abstract
In mobile social groups, influence patterns driving group movement can vary between democratic and despotic. The arrival at any single pattern of influence is thought to be underpinned by environmental factors and group composition. To determine the specific patterns of influence in a chacma baboon troop we used spatially explicit data to identify patterns of individual movement bias on travel decision-making. We scaled these estimates of individual-level bias to the group as a whole by constructing an influence network, and assess its emergent structural properties. Our results suggest that individual animals respond consistently to specific group members, and that higher-ranking animals are more likely to influence the movement of others. At the group level, we identified a network structure with a single core and two outer shells. The presence of a core in this troop suggests that a set of highly inter-dependent individuals drive routine group movements. Our findings suggest that heterogeneity in individual level movement bias can lead to group level influence structures, and that movement patterns in mobile social groups can add to the exploration of both how social influence patterns develop (i.e., mechanistic aspects) and their linkages to individual and group level outcomes (i.e., functional aspects).
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".