Network integration and limits to social inheritance in vervet monkeys
Bibliographic record
Abstract
, 12084 (doi:10.1038/ncomms12084)) has demonstrated that network structure can be maintained by a simple process of social inheritance. Here, we ask how juvenile vervet monkeys integrate into their adult grooming networks, using the model to test whether observed grooming patterns replicate network structure. Female juveniles, who are philopatric, increased their grooming effort towards adults more than males, although this was not reciprocated by the adults themselves. While more consistent maternal grooming networks, together with maternal network strength, predicted increasing similarity in the patterning of mother-daughter grooming allocations, daughters' grooming networks generally did not match closely those of their mothers. However, maternal networks themselves were not very consistent across time, thus presenting youngsters with a moving target that may be difficult to match. Observed patterns of juvenile female grooming did not replicate the adult network, for which increased association with adults not groomed by their mothers would be necessary. These results suggest that network flexibility, not stability, characterizes our groups and that juveniles are exposed to, and must learn to cope with, temporal shifts in network structure. We hypothesize that this may lead to individual variation in behavioural flexibility, which in turn may help explain why and how variation in sociability influences fitness.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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".