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Vigilance and grouping in the southern African ground squirrel (<i>Xerus inauris</i>)

2011· article· en· W1704291021 on OpenAlexafffund
Sarah Edwards, Jane M. Waterman

Bibliographic record

VenueAfrican Journal of Ecology · 2011
Typearticle
Languageen
FieldEnvironmental Science
TopicAnimal Ecology and Behavior Studies
Canadian institutionsUniversity of Manitoba
FundersNatural Sciences and Engineering Research Council of CanadaUniversity of Central FloridaNational Science Foundation
KeywordsVigilance (psychology)ForagingGround squirrelPredationPsychologyBiologyEcologyCognitive psychologySquirrel monkey

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.452

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.026
GPT teacher head0.224
Teacher spread0.197 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations23
Published2011
Admission routes2
Has abstractyes

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