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Record W4366813874 · doi:10.1093/beheco/arad030

Vocal performance increases rapidly during the dawn chorus in Adelaide’s warbler (<i>Setophaga adelaidae</i>)

2023· article· en· W4366813874 on OpenAlexafffund
Juleyska Vazquez‐Cardona, Tyler R. Bonnell, Peter C. Mower, Orlando J. Medina, Hester Jiskoot, David M. Logue

Bibliographic record

VenueBehavioral Ecology · 2023
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAnimal Vocal Communication and Behavior
Canadian institutionsUniversity of Lethbridge
FundersNatural Sciences and Engineering Research Council of CanadaUniversity of Lethbridge
KeywordsChorusSingingMorningBiologyAudiologyAcousticsLiteratureArtBotanyPhysicsMedicine

Abstract

fetched live from OpenAlex

Abstract Many songbirds sing intensely during the early morning, resulting in a phenomenon known as the dawn chorus. We tested the hypothesis that male Adelaide’s warblers (Setophaga adelaidae) warm up their voices during the dawn chorus. If warming up the voice is one of the functions of the dawn chorus, we predicted that vocal performance would increase more rapidly during the dawn chorus compared to the rest of the morning and that high song rates during the dawn chorus period contribute to the increase in vocal performance. The performance metrics recovery time, voiced frequency modulation, and unvoiced frequency modulation were low when birds first began singing, increased rapidly during the dawn chorus, and then leveled off or gradually diminished after dawn. These changes are attributable to increasing performance within song types. Reduction in the duration of the silent gap between notes is the primary driver of improved performance during the dawn chorus. Simulations indicated that singing at a high rate during the dawn chorus period increases performance in two of the three performance measures (recovery time and unvoiced frequency modulation) relative to singing at a low rate during this period. These findings are consistent with the hypothesis that vocal warm-up is one benefit of participation in the dawn chorus.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.228
Threshold uncertainty score0.672

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 studies0.0000.000
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 head0.026
GPT teacher head0.294
Teacher spread0.268 · 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 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

Citations11
Published2023
Admission routes2
Has abstractyes

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