Individual Factors Predicting the Disappearance and Reproductive Success of Vervet Monkeys (Chlorocebus pygerythrus)
Bibliographic record
Abstract
Social network analysis (SNA) is an increasingly popular method of quantifying social interactions and relating these to individual characteristics. However, few studies have considered how demographic events influence social networks and how social position affects fitness among species that live around humans. Using the gambit of the group and proximity data, I performed SNA on vervet monkeys to determine how social centrality predicted which individuals were more likely to disappear and to have infants that survived past one year. Older males with a lower or decrease in social centrality were more likely to disappear, where older males were more likely to emigrate, and individuals who decreased in their eigenvector centrality were more likely to have a human-related death. Females with a greater betweenness tended to have greater infant survival rates. Overall, emigration was influenced by natural history while human-related disappearances and reproductive success were mediated by social position.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".