Dyadic associations and individual sociality in bighorn ewes
Bibliographic record
Abstract
Sociality presumably evolved because it leads to fitness benefits; yet we know little about what drives individual variability in sociality, particularly with respect to hierarchical levels of social organization. Social network architecture is based upon dyadic interactions, but the factors affecting pairwise relationships are not necessarily those affecting higher-level network-derived measures of social behavior. We examined the influence of relatedness, age, dominance, and reproductive status on proximal associations and social network centrality of individuals in the fission–fusion society of bighorn ewes (Ovis canadensis) at Ram Mountain, Canada. From 2011 to 2013, 63–81% of adult ewes were equipped with proximity loggers, recording when they were within 1.5 m of one another. Ewe social structure was not random and individuals exhibited a tendency to have proximal associations that were consistent across years. Age and reproductive status appeared to have a weak effect on network centrality, but this effect was largely absent for frequency of proximal association. Furthermore, we found no effect of dominance rank on either proximal associations or network centrality. We speculate that interannual variation in these relationships may be indicative of predation affecting social dynamics. The disconnect between determinants that affect the costs and benefits of dyadic associations and those that emerge from network-derived behaviors highlights the importance of testing effects at multiple levels of social organization in animal societies.
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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 | 0.000 | 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 | 0.000 |
| 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".