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Record W2538621717 · doi:10.1111/eth.12566

Individual, Group, and Environmental Influences on Helping Behavior in a Social Carnivore

2016· article· en· W2538621717 on OpenAlexaff
David E. Ausband, Michael S. Mitchell, Sarah B. Bassing, Andrea T. Morehouse, Douglas W. Smith, Daniel R. Stahler, Jennifer Struthers

Bibliographic record

VenueEthology · 2016
Typearticle
Languageen
FieldEnvironmental Science
TopicWildlife Ecology and Conservation
Canadian institutionsUniversity of Alberta
FundersIdaho Department of Fish and GameEppley Foundation for ResearchUniversity of Montana
KeywordsCanisPredationAffect (linguistics)Cooperative breedingGray wolfPredatorCarnivoreEcologyHelping behaviorAbundance (ecology)Gray (unit)DemographyPsychologyBiologySocial psychologyCommunication

Abstract

fetched live from OpenAlex

Abstract Variation in group composition and environment can affect helping behavior in cooperative breeders. Understanding of how group size, traits of individuals within groups, food abundance, and predation risk simultaneously influence helping behavior is limited. We evaluated pup‐guarding behavior in gray wolves ( Canis lupus ) to assess how differences in individuals, groups, and environment affect helping behavior. We used data from 92 GPS ‐collared wolves in North America (2001–2012) to estimate individual pup‐guarding rates. Individuals in groups with low helper‐to‐pup ratios spent more time guarding young than those in groups with more helpers, an indication of load‐lightening. Female helpers guarded more than male helpers, but this relationship weakened as pups grew. Subset analyses including data on helper age and wolf and prey density showed such factors did not significantly influence pup‐guarding rates. We show that characteristics of individuals and groups have strong influences on pup‐guarding behavior in gray wolves, but environmental factors such as food abundance and predation risk from conspecifics were not influential.

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)0.000
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 score0.013
Threshold uncertainty score0.590

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.020
GPT teacher head0.260
Teacher spread0.239 · 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

Citations31
Published2016
Admission routes1
Has abstractyes

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