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Record W2508437222 · doi:10.1121/1.4962281

An investigation of sex differences in acoustic features in black-capped chickadee (<i>Poecile atricapillus</i>) <i>chick-a-dee</i> calls

2016· article· en· W2508437222 on OpenAlexaff
Kimberley A. Campbell, Allison H. Hahn, Jenna V. Congdon, Christopher B. Sturdy

Bibliographic record

VenueThe Journal of the Acoustical Society of America · 2016
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAnimal Vocal Communication and Behavior
Canadian institutionsWomen and Children’s Health Research InstituteUniversity of Alberta
Fundersnot available
KeywordsDiscriminant function analysisBiologyCategorizationZoologyStatisticsMathematicsComputer scienceArtificial intelligence

Abstract

fetched live from OpenAlex

Sex differences have been identified in a number of black-capped chickadee vocalizations and in the chick-a-dee calls of other chickadee species [i.e., Carolina chickadees (Poecile carolinensis)]. In the current study, 12 acoustic features in black-capped chickadee chick-a-dee calls were investigated, including both frequency and duration measurements. Using permuted discriminant function analyses, these features were examined to determine if any features could be used to identify the sex of the caller. Only one note type (A notes) classified male and female calls at levels approaching significance. In particular, a permuted discriminant function analysis revealed that the start frequency of A notes best allowed for categorization between the sexes compared to any other acoustic parameter. This finding is consistent with previous research on Carolina chickadee chick-a-dee calls that found that the starting frequency differed between male- and female-produced A notes [Freeberg, Lucas, and Clucas (2003). J. Acoust. Soc. Am. 113, 2127-2136]. Taken together, these results and the results of studies with other chickadee species suggest that sex differences likely exist in the chick-a-dee call, specifically acoustic features in A notes, but that more complex features than those addressed here may be associated with the sex of the caller.

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 score0.811
Threshold uncertainty score0.384

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 integrity0.0000.000
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 head0.014
GPT teacher head0.258
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 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

Citations7
Published2016
Admission routes1
Has abstractyes

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