Human Activity Dampens the Benefits of Group Size on Vigilance in Khulan (Equus hemionus) in Western China
Bibliographic record
Abstract
Animals receive anti-predator benefits from social behavior. As part of a group, individuals spend less time being vigilant, and vigilance decreases with increasing group size. This phenomenon, called "the many-eyes effect", together with the "encounter dilution effect", is considered among the most important factors determining individual vigilance behavior. However, in addition to group size, other social and environmental factors also influence the degree of vigilance, including disturbance from human activities. In our study, we examined vigilance behavior of Khulans (Equus hemionus) in the Xinjiang Province in western China to test whether and how human disturbance and group size affect vigilance. According to our results, Khulan showed a negative correlation between group size and the percentage time spent vigilant, although this negative correlation depended on the groups' disturbance level. Khulan in the more disturbed area had a dampened benefit from increases in group size, compared to those in the undisturbed core areas. Provision of continuous areas of high-quality habitat for Khulans will allow them to form larger undisturbed aggregations and to gain foraging benefits through reduced individual vigilance, as well as anti-predator benefits through increased probability of predator detection.
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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 | 0.000 |
| 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) | 0.001 | 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".