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Record W2121488717 · doi:10.1098/rspb.2010.2037

A latitudinal gradient in rates of evolution of avian syllable diversity and song length

2010· article· en· W2121488717 on OpenAlexafffund
Jason T. Weir, David Wheatcroft

Bibliographic record

VenueProceedings of the Royal Society B Biological Sciences · 2010
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAnimal Vocal Communication and Behavior
Canadian institutionsThe Scarborough HospitalUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of CanadaUniversity of TorontoNational Geographic SocietyNational Science Foundation
KeywordsSyllableDiversity (politics)GeographyBiologyLinguisticsPhilosophy

Abstract

fetched live from OpenAlex

We ask whether rates of evolution in traits important for reproductive isolation vary across a latitudinal gradient, by quantifying evolutionary rates of two traits important for pre-mating isolation-avian syllable diversity and song length. We analyse over 2500 songs from 116 pairs of closely related New World passerine bird taxa to show that evolutionary rates for the two main groups of passerines-oscines and suboscines-doubled with latitude in both groups for song length. For syllable diversity, oscines (who transmit song culturally) evolved more than 20 times faster at high latitudes than in low latitudes, whereas suboscines (whose songs are innate in most species and who possess very simple song with few syllable types) show no clear latitudinal gradient in rate. Evolutionary rates in oscines and suboscines were similar at tropical latitudes for syllable complexity as well as for song length. These results suggest that evolutionary rates in traits important to reproductive isolation and speciation are influenced by latitude and have been fastest, not in the tropics where species diversity is highest, but towards the poles.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.036
GPT teacher head0.273
Teacher spread0.236 · 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 source (direct Gemma or distilled Codex), 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

Citations111
Published2010
Admission routes2
Has abstractyes

Explore more

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