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Record W2021004614 · doi:10.1071/mu13034

Relative brain size in Australian birds

2014· article· en· W2021004614 on OpenAlexaff
Donald C. Franklin, Stephen T. Garnett, Gary Luck, Cristián Gutiérrez‐Ibáñez, Andrew N. Iwaniuk

Bibliographic record

VenueEmu - Austral Ornithology · 2014
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAnimal Behavior and Reproduction
Canadian institutionsUniversity of LethbridgeUniversity of Alberta
FundersAustralian Research CouncilMonash UniversityNational Climate Change Adaptation Research FacilitySmithsonian Institution
KeywordsPasserineAllometryBiologyOrnithologySouthern HemisphereConservation biologyEcologyZoologyBrain sizeRegression analysisRegressionStatisticsMathematics

Abstract

fetched live from OpenAlex

Among species, relative brain size (RBS) is correlated with aspects of ecology and behaviour. We analysed patterns of RBS in Australian birds based on 3164 measurements of brain size in 504 species, and provide species-level data for further analysis. Regression slopes calculated both with and without phylogenetic correction are provided for all species and for well-represented orders and passerine families. Patterns of brain-size allometry differ among orders but the evidence for variation among passerine families is equivocal, depending on the method of analysis. These differences are attributable both to absolute differences in RBS corresponding to different regression intercepts and to different regression slopes. Allometric patterns in Australian birds are virtually identical to those reported elsewhere, with large RBS in parrots, cockatoos and owls, and particularly small RBS in galliforms, dromaiids, grebes, swifts and swallows. Our data can be used to generate hypotheses about the drivers of RBS in particular avian groups. For example, small RBS in unrelated aerial foragers suggests that physical constraints may influence the evolution of RBS.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.522
Threshold uncertainty score1.000

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)0.0010.000

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 head0.030
GPT teacher head0.269
Teacher spread0.239 · 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.

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

Citations25
Published2014
Admission routes1
Has abstractyes

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