Correction: Investigating Differences in Vigilance Tactic Use within and between the Sexes in Eastern Grey Kangaroos
Bibliographic record
Abstract
Aggregation is thought to enhance an animal's security through effective predator detection and the dilution of risk.A decline in individual vigilance as group size increases is commonly reported in the literature and called the group size effect.However, to date, most of the research has only been directed toward examining whether this effect occurs at the population level.Few studies have explored the specific contributions of predator detection and risk dilution and the basis of individual differences in the use of vigilance tactics.We tested whether male and female (non-reproductive or with young) eastern grey kangaroos (Macropus giganteus) adopted different vigilance tactics when in mixed-sex groups and varied in their reliance on predator detection and/or risk dilution as group size changed.This species exhibits pronounced sexual dimorphism with females being much smaller than males, making them differentially vulnerable toward predators.We combined field observations with vigilance models describing the effects of detection and dilution on scanning rates as group size increased.We found that females with and without juveniles relied on predator detection and risk dilution, but the latter adjusted their vigilance to the proportion of females with juveniles within their group.Two models appeared to equally support the data for males suggesting that males, similarly to females, relied on predator detection and risk dilution but may also have adjusted their vigilance according to the proportion of mothers within their group.Differential vulnerability may cause sex differences in vigilance tactic use in this species.The presence of males within a group that do not, or only partially, contribute to predator detection and are less at risk may cause additional security costs to females.Our results call for reexamination of the classical view of the safety advantages of grouping to provide a more detailed functional interpretation of gregariousness.
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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 | 0.009 | 0.152 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.182 | 0.033 |
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".