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Record W2041513175 · doi:10.1121/1.1635838

Sex differences in the acoustic structure of vowel-like grunt vocalizations in baboons and their perceptual discrimination by baboon listeners

2003· article· en· W2041513175 on OpenAlexaff
Drew Rendall, Michael J. Owren, Elise M. Weerts, Robert D. Hienz

Bibliographic record

VenueThe Journal of the Acoustical Society of America · 2003
Typearticle
Languageen
FieldPsychology
TopicPhonetics and Phonology Research
Canadian institutionsUniversity of Lethbridge
FundersNational Institute on Drug AbuseDepartment of Home Affairs
KeywordsFormantBaboonVowelPerceptionContext (archaeology)AudiologyPsychologyAdult maleAcousticsSpeech recognitionBiologyComputer scienceMedicineNeurosciencePhysics

Abstract

fetched live from OpenAlex

This study quantifies sex differences in the acoustic structure of vowel-like grunt vocalizations in baboons (Papio spp.) and tests the basic perceptual discriminability of these differences to baboon listeners. Acoustic analyses were performed on 1028 grunts recorded from 27 adult baboons (11 males and 16 females) in southern Africa, focusing specifically on the fundamental frequency (F0) and formant frequencies. The mean F0 and the mean frequencies of the first three formants were all significantly lower in males than they were in females, more dramatically so for F0. Experiments using standard psychophysical procedures subsequently tested the discriminability of adult male and adult female grunts. After learning to discriminate the grunt of one male from that of one female, five baboon subjects subsequently generalized this discrimination both to new call tokens from the same individuals and to grunts from novel males and females. These results are discussed in the context of both the possible vocal anatomical basis for sex differences in call structure and the potential perceptual mechanisms involved in their processing by listeners, particularly as these relate to analogous issues in human speech production and perception.

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: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.629
Threshold uncertainty score0.462

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 studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.022
GPT teacher head0.293
Teacher spread0.271 · 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 designQualitative
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

Citations81
Published2003
Admission routes1
Has abstractyes

Explore more

Same venueThe Journal of the Acoustical Society of AmericaSame topicPhonetics and Phonology ResearchFrench-language works237,207