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Record W4392136706 · doi:10.1002/ajp.23610

Edge effects and social behavior in three platyrrhines

2024· article· en· W4392136706 on OpenAlexaff
Laura M. Bolt, Jenna L. Owens, M. Grant, Elizabeth Coggeshall, Dorian G. Russell, Carrie Merrigan‐Johnson, Zachary S. Jacobson, Zachary T. Schmidt, Francesca V E Kaser, Amy L. Schreier

Bibliographic record

VenueAmerican Journal of Primatology · 2024
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAnimal Behavior and Reproduction
Canadian institutionsUniversity of CalgaryUniversity of Toronto
FundersEunice Kennedy Shriver National Institute of Child Health and Human Development
KeywordsEnhanced Data Rates for GSM EvolutionBiologyComputer science

Abstract

fetched live from OpenAlex

Social behavior is a key adaptation for group-living primates. It is important to assess changes to social behavior in human-impacted landscape zones to better understand the impact of anthropogenic disturbance on primate species. We investigated social behavior rate and type in three species of platyrrhines across 100 m anthropogenic edge and interior zones of a fragmented forest in Costa Rica, La Suerte Biological Research Station (LSBRS). Following results from other sites, we predicted that spider monkeys (Ateles geoffroyi), capuchin monkeys (Cebus imitator) and howler monkeys (Alouatta palliata) would show lower rates and fewer types of social behavior in forest edge compared to interior. We collected 1341 h of instantaneous focal data from 2017 to 2023 across the three monkey species. We found mixed support for our predictions, with spider and capuchin monkeys modifying some but not all aspects of social behavior across forest zones at LSBRS. Spider monkeys had lower rates of social behavior and capuchin monkeys performed different types of social behaviors in forest edge compared to interior at LSBRS. In contrast, howler monkeys did not modify social behavior. Two out of three platyrrhine species altered their social behavior when in anthropogenic edges, indicating behavioral adjustment when in human-altered habitat areas at LSBRS.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.605
Threshold uncertainty score0.104

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)0.0000.000

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 head0.012
GPT teacher head0.256
Teacher spread0.244 · 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

Citations7
Published2024
Admission routes1
Has abstractyes

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