Vigilance and grouping in the southern African ground squirrel (<i>Xerus inauris</i>)
Bibliographic record
Abstract
Abstract Animals may form groups in response to the foraging–vigilance trade‐off, through enhanced predator detection (collective detection hypothesis) or reduced predation risk to the individual (dilution hypothesis), allowing individuals to decrease vigilance levels. Both hypotheses predict decreasing individual vigilance levels with increasing group size; however, the collective detection hypothesis also predicts increasing overall group vigilance with increasing group size. However, in species in which vigilance and foraging are not mutually exclusive, where vigilance may not be as costly, neither of these hypotheses may apply. Here, we examine the relationship between group size and vigilance in the social Cape ground squirrel (Xerus inauris), a species that can combine foraging and vigilance behaviours. Ten groups were observed using scan sampling, measuring both group and individual vigilance and group size. A negative relationship existed between individual vigilance and group size and a positive relationship between group vigilance and group size. Therefore, in Cape ground squirrels, vigilance seems to be costly even though it can be combined with foraging behaviours. Furthermore, group vigilance behaviour gives support to the collective detection hypothesis, whilst individual vigilance gives support to both hypotheses.
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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".