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Record W2152983237 · doi:10.1676/08-008.1

Vocal Repertoires of Auklets (Alcidae: Aethiini): Structural Organization and Categorization

2009· article· en· W2152983237 on OpenAlexaff
Sampath S. Seneviratne, Ian L. Jones, Edward H. Miller

Bibliographic record

VenueThe Wilson Journal of Ornithology · 2009
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAnimal Vocal Communication and Behavior
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsBiologyZoologyParakeetRepertoireEcology

Abstract

fetched live from OpenAlex

We categorized and quantified the complete vocal repertoires of breeding adult auklets (Aethiini, 5 species) in their breeding areas to provide a baseline for comparative study of the structure and function of vocalizations within this monophyletic group of seabirds. We recognized 22 call types across species and 3–5 call types for each species. Calls were characterized by one to five frequency modulated, harmonically rich note types arranged sequentially in varied combinations. Frequency attributes varied more than temporal attributes within and across species. Repertoires and display complexity of nocturnal and diurnal species did not differ consistently. We recognized two major forms of vocal display: alternating arrangement of note types (Cassin's Auklet [Ptychoramphus aleuticus] and Parakeet Auklet [Aethia psittacula]); and sequentially graded arrangement of note types (Least Auklet [A. pusilla] and Whiskered Auklet [A. pygmaea]). One species' repertoire (Crested Auklet [A. cristatella]) was composed of a mix of the two forms of display. There were vocal homologies in frequency modulation of notes, arrangement of notes, and note type composition of displays. Our analysis revealed vocal similarities between: (1) two species not normally grouped together (Cassin's and Parakeet auklets); and (2) Whiskered and Crested auklets, which have been suggested previously to be closely related.

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 score0.695
Threshold uncertainty score0.193

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 science0.0000.000
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 head0.009
GPT teacher head0.271
Teacher spread0.262 · 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

Citations26
Published2009
Admission routes1
Has abstractyes

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