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Record W2600576312 · doi:10.22621/cfn.v130i4.1919

Exploring the Adaptive Significance of Five Types of Puma (<i>Puma concolor</i>) Vocalizations

2017· article· en· W2600576312 on OpenAlexvenueno aff
Maximilian L. Allen, Christopher C. Wilmers

Bibliographic record

VenueThe Canadian Field-Naturalist · 2017
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAnimal Vocal Communication and Behavior
Canadian institutionsnot available
FundersGordon and Betty Moore FoundationCalifornia Department of Fish and WildlifeNational Science Foundation
KeywordsPumaALARMCommunicationVocal communicationAnimal communicationAlarm signalVariety (cybernetics)PsychologyBiologyComputer scienceArtificial intelligence

Abstract

fetched live from OpenAlex

Communication is a central component of animal behaviour, yet communicative behaviours are poorly studied due to their complexity and varied functions. Pumas (Puma concolor) are wide-ranging, solitary felids that primarily use indirect cues (e.g., scent marking) for communication. Because these cryptic carnivores are rarely observed directly, little is known about their vocalizations in the wild. We recorded a variety of Puma vocalizations among females and family groups using motion-triggered video cameras and then attempted to understand the function of each vocalization. We found two categories of vocalizations: 1) attention-attracting (caterwauling and mewing), and 2) calls (contact, agitated, and alarm). Vocalizations to attract attention ranged across broad frequencies. Contact, agitated, and alarm calls are narrow-frequency vocalizations that varied in intensity and were used to communicate with nearby conspecifics. Vocal communication entails risk, and while some Puma vocalizations may provide benefits that outweigh their risk, others are structured to limit detection and risk. These observations highlight the importance of the structure of vocalizations used during different behaviours to understand their adaptive significance.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.500
Threshold uncertainty score0.988

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 studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.067
GPT teacher head0.287
Teacher spread0.220 · 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

Citations12
Published2017
Admission routes1
Has abstractyes

Explore more

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