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Record W2948960484 · doi:10.21199/wb50.2.1

STRUCTURE OF LARK SPARROW SONG IN CALIFORNIA

2019· article· en· W2948960484 on OpenAlexaff
Ed Pandolfino, Richard Hedley

Bibliographic record

VenueWestern Birds · 2019
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAnimal Vocal Communication and Behavior
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsSparrowGeographyEcologyBiology

Abstract

fetched live from OpenAlex

We studied the song of the lark Sparrow (Chondestes grammacus) in recordings of 15 individuals from a variety of locations in California. lark Sparrow song is delivered as variable sequences structured in a hierarchy of four levels: elements, syllables, strophes, and themes. at the simplest level, elements can be distinguished by their appearance on a spectrogram. Birds vary the number of repetitions of each element to produce a syllable and string together several syllables to produce a strophe (mean 5.9 syllables per strophe). Strophes are delivered at an average rate of 5.4 per minute. Strophes can be further classifid as belonging to one of a few themes in a male’s repertoire, where strophes in different themes are composed of almost entirely distinct elements and syllables. in <6% of strophes did we fid elements from one theme mixed with elements from a different theme. Each individual sang one to three themes. The size of the repertoire of strophes is large but we could not quantify it because within a continuous bout of singing any particular sequence of syllables was repeated in only 5% of strophes. Each theme comprised roughly 20–40 distinct syllables. Thus an individual singing three themes could have a repertoire of 60–120 syllables. The number of unique elements per theme ranged from 11 in birds revealing just one theme up to 39 in birds revealing three themes, but longer recordings may have yielded more elements per bird. if our observations for single-themed birds may be extrapolated, birds with three themes might have repertoires of 33–51 elements. in the two comparisons possible, we detected almost no sharing of elements among neighboring birds.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.102
Threshold uncertainty score0.235

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 head0.011
GPT teacher head0.264
Teacher spread0.253 · 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

Citations1
Published2019
Admission routes1
Has abstractyes

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