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Record W4393993742 · doi:10.33265/polar.v43.9430

What does the fox say? Arctic fox vocalization and associated den behaviours

2024· article· en· W4393993742 on OpenAlexafffund
Kayla J. Buhler, Gustaf Samelius, Ray T. Alisauskas, Emily Jenkins

Bibliographic record

VenuePolar Research · 2024
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAnimal Vocal Communication and Behavior
Canadian institutionsEnvironment and Climate Change CanadaUniversity of Saskatchewan
FundersNatural Sciences and Engineering Research Council of CanadaArcticNetPolar Knowledge Canada
KeywordsArcticGeographyArctic foxBiologyEcology

Abstract

fetched live from OpenAlex

Foxes (Vulpes spp.) are small, solitary canids with relatively low social complexity compared to more gregarious canids, such as wolves and dogs. They are, therefore, expected to have a relatively simple vocal repertoire, with limited low-intensity sounds for close communication and many high-intensity sounds for long-distance communication. Arctic foxes (Vulpes lagopus), like many other foxes, are largely solitary outside of the breeding season. However, they have the largest litter size in the order Carnivora and may experience enhanced social complexity during the breeding season. In this study, we document the vocal repertoire of the Arctic fox during the breeding season, and how it changes before and after the emergence of pups. We also describe the relationship between vocalizations and other denning behaviours. Camera-traps captured six distinct sounds produced by breeding pairs of Arctic foxes and their young at dens: territorial barks, warning barks, alarm calls, cooing, whines and growling. Our study shows that although high-intensity sounds, such as territorial barks, are an important form of long-distance communication among Arctic foxes, low-intensity sounds and sound mixing are used on their dens following pup emergence. Thus, Arctic fox vocalization may be more complex than previously documented.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.629
Threshold uncertainty score0.368

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 studies0.0000.000
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 head0.066
GPT teacher head0.405
Teacher spread0.340 · 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 designBench or experimental
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

Citations3
Published2024
Admission routes2
Has abstractyes

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