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Record W1544291168

Patterns of vocal divergence in a group of non-oscine birds (auklets; Alcidae, Charadriiformes)

2012· article· en· W1544291168 on OpenAlexaff
Sampath S. Seneviratne, Ian L. Jones, Steven M. Carr

Bibliographic record

VenueEvolutionary ecology research · 2012
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAnimal Vocal Communication and Behavior
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsCharadriiformesBiologyPhylogeneticsZoologyCladeMonophylyEcologyPhylogenetic treeEvolutionary biologyDivergence (linguistics)CladisticsTaxon
DOInot available

Abstract

fetched live from OpenAlex

Question: Are phylogenetic relationships the major determinant of vocal relationships in non-oscine birds (birds that do not have a learning component in the vocalization)? Background: Both environmental variables and phylogenetic affinities can affect vocalizations. Unlearned vocalizations are characteristics of most non-oscine bird families, which have a relatively less-complex syrinx and vocalizations. Organism: A monophyletic group of underground-nesting seabirds (auklets: Aethiini, Alcidae, Charadriiformes) from the Aleutian Islands, Alaska, USA. Methods: We mapped vocal characters (28 acoustic and 10 syringeal) from total repertoires of all members of the tribe Aethiini onto a molecular phylogeny to compare the relative influence of phylogeny and breeding habitat on vocal divergence. Conclusion: Phylogeny, visual display, and ecological factors have contributed to vocal divergence in this clade. Temporal attributes and syringeal attributes of the acoustics of vocalization showed high congruence with phylogeny. Frequency attributes, which are affected by environmental variables, showed low congruence, and therefore high homoplasy.

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)0.001
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 score0.210
Threshold uncertainty score0.346

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 science0.0000.001
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 head0.053
GPT teacher head0.376
Teacher spread0.323 · 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

Citations13
Published2012
Admission routes1
Has abstractyes

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