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Alula size signals male condition and predicts reproductive performance in an Arctic‐breeding passerine

2013· article· en· W2115327412 on OpenAlexafffund
Sarah Guindre‐Parker, H. Grant Gilchrist, Sarah Baldo, Oliver P. Love

Bibliographic record

VenueJournal of Avian Biology · 2013
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAnimal Behavior and Reproduction
Canadian institutionsUniversity of Windsor
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsPlumagePasserineAchromatic lensBiologyZoologySeasonal breederOffspringOrnamentsSexual selectionReproductive successMate choiceEcologyGeneticsDemographyOpticsPhysics

Abstract

fetched live from OpenAlex

While studies of achromatic plumage signaling are scarce relative to chromatic ornaments, achromatic ornaments have the potential to act as an efficient form of visual communication due to the highly conspicuous contrast between black and white body regions. Recently, achromatic plumage reflectance has been shown to indicate condition, yet the condition‐dependence of achromatic patch size remains unstudied. Here we show the first evidence that alula size, an achromatic plumage patch, has the potential to signal a male’s condition and predict reproductive performance. In Arctic‐breeding snow buntings Plectrophenax nivalis , the size of the alula simultaneously predicted pre‐breeding physiological health and the number of offspring produced, through an intermediate variable (lay date). Snow buntings appear to pair assortatively; males and females arriving earlier pair together, and changes in body condition over the breeding season are positively related within pairs. We suggest that simple achromatic plumage patches, like alula size, have the potential to act as condition‐dependent signals. Consequently, females may benefit from assessing these signals to reliably evaluate a male’s condition and reproductive potential as a means of maximizing their reproductive success.

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 score0.562
Threshold uncertainty score0.365

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 head0.024
GPT teacher head0.250
Teacher spread0.226 · 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

Citations21
Published2013
Admission routes2
Has abstractyes

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