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Record W2942268673 · doi:10.1121/1.5101619

Sex-differences in timing of the black-capped chickadee fee-bee song

2019· article· en· W2942268673 on OpenAlexaff
Anastasiya Kobrina, Allison H. Hahn, Eduardo Mercado, Christopher B. Sturdy

Bibliographic record

VenueThe Journal of the Acoustical Society of America · 2019
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAnimal Vocal Communication and Behavior
Canadian institutionsUniversity of AlbertaNorQuest College
Fundersnot available
KeywordsAttractionZoologySound productionAnimal communicationMate choiceDuration (music)BiologyDemographyCommunicationPsychologyMatingArtAcousticsSociologyLiterature

Abstract

fetched live from OpenAlex

Male black-capped chickadees produce fee-bee songs in spring for mate attraction and territorial defense. Less is known about the female song use in this species although a version of song, soft-song, appears to be used in mate-mate communication. Recent analyses of songs produced by chickadees revealed that female fee-bee songs are distinct from male songs in the spectral domain. Chickadees also precisely control the timing of their fee-bee songs during territory defense. No previous work has explored whether there are sex differences in the temporal patterning of fee-bee song production. Inter-song intervals were extracted from recordings of fee-bee songs produced in non-social contexts by 8 male and 7 female birds. Male chickadees produced fee-bee songs regularly, with the majority of songs spaced at intervals of 2.5–5.0 s. Song timing by females was more variable with production intervals ranging from 1.5 to 8.0 s. The relative stereotypy of song timing by males is consistent with earlier work suggesting that males may modulate song timing to communicate with other birds (e.g., by timing song production to either reduce or increase the likelihood that their songs overlap with those of other singers), and with differential use of songs by males and females.

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 score0.615
Threshold uncertainty score0.222

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 studies0.0000.001
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 head0.022
GPT teacher head0.271
Teacher spread0.249 · 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

Citations1
Published2019
Admission routes1
Has abstractyes

Explore more

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