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Record W4400797529 · doi:10.1080/09524622.2024.2366924

The content of reindeer male vocalisations: acoustic cues to age and size

2024· article· en· W4400797529 on OpenAlexafffund
Laura Puch, Robert B. Weladji, Øystein Holand, Jouko Kumpula

Bibliographic record

VenueBioacoustics · 2024
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAnimal Vocal Communication and Behavior
Canadian institutionsConcordia University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsCommunicationContent (measure theory)PsychologyCognitive psychologyBiologyMathematics

Abstract

fetched live from OpenAlex

Some acoustic parameters of animal vocalisations have been shown to reliably indicate male quality and play a role in mate and rival assessment. Reindeer possess a peculiar vocal tract anatomy involving a laryngeal air sac which probably acts as an additional filter, making it a candidate species for novel investigations in the field of bioacoustics. We investigated whether some acoustic parameters of male rutting vocalisations were good indicators of age and body weight (used as an index for body size). We did this by performing acoustic analyses using recordings collected from a semi-domesticated reindeer population in northern Finland. We found the age of subadult males (aged 2.5–4.5 years) to be negatively correlated with formant F3 and formant spacing, suggesting that their vocalisations convey information on the caller’s age. Individual formant frequencies were not affected by male body weight, but formant spacing was lower in heavier males. Despite the presence of the laryngeal air sac, formant spacing seems to be an acoustic parameter influencing mate and rival assessment in reindeer as it gives an honest indication of male body size. We discuss the importance of reliable acoustic cues to quality indices in sexual selection contexts.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
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 head0.049
GPT teacher head0.303
Teacher spread0.254 · 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 source (direct Gemma or distilled Codex), 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

Citations1
Published2024
Admission routes2
Has abstractyes

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