Bibliographic record
Abstract
Vigilance has been predicted to decrease with group size due to increased predator detection and dilution of predation risk in larger groups. Although earlier literature reviews have provided ample support for this prediction, an increasing number of studies have failed to document a decline in vigilance with group size. In addition, support for this prediction has been based thus far on the P value of the relationship between vigilance and group size rather than on a quantitative assessment of effect magnitude. Here, I use a meta-analysis of empirical relationships between vigilance and group size in birds published in the last 35 years to provide a reassessment of the group-size effect on vigilance. Nearly one-third of all published relationships between vigilance and group size were not significant (n = 172). Results from the meta-analysis indicate weak to moderate negative correlations between group size and time spent vigilant (n = 43), scan frequency (n = 29), or scan duration (n = 20). The magnitude of the relationship was stronger in studies that controlled the amount of food available to birds. A funnel plot of the relationship between correlation coefficients and sample size failed to reveal an obvious publication bias. Although the meta-analysis results generally support the prediction that vigilance should decline with group size, a large amount of variation in vigilance remains unexplained in avian studies.
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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 | 0.036 | 0.107 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.006 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".