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Record W4242254188 · doi:10.1093/auk/122.1.71

Protogyny in Autumn Migration: Do Male Birds ”Play Chicken”?

2005· article· en· W4242254188 on OpenAlexaff
Alexander M. Mills

Bibliographic record

VenueThe Auk · 2005
Typearticle
Languageen
FieldEnvironmental Science
TopicAvian ecology and behavior
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsBiologySexual selectionEcologySpring (device)Selection (genetic algorithm)ZoologyTaxon

Abstract

fetched live from OpenAlex

Abstract Protandry, the earlier arrival of males than of females on breeding areas, occurs in many taxa, including many migratory birds. Numerous hypotheses have been generated to explain protandry. Using bird-banding records, I show that protogyny, the earlier migration of females, frequently occurs in the autumn, though it is less universal and less dramatic than spring protandry. In one species, it occurs in both hatch-year and adult birds. When (1) spring and autumn, (2) departures and arrivals, and (3) breeding and wintering ground conditions are considered, hypotheses generated only to explain spring protandry can be more thoroughly evaluated. Using that approach, the most parsimonious explanation of differential migration between the sexes explains earlier male arrival in spring and later male departure in autumn through either (1) indirect selection operating on intrasexual male competition for territories or (2) direct selection operating on intersexual relations requiring males to be present on breeding territories when females are present. In autumn-protogynous species, males may ”play chicken,” balancing the benefits of remaining longer than females and protecting territories for subsequent years against the costs of remaining in the north under deteriorating conditions and delaying the acquisition of a good winter territory. Protogynie et migration automnale: Est-ce que les mâles ”jouent les dégonflés”?

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 categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.028
Threshold uncertainty score0.995

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)0.0140.005

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 head0.009
GPT teacher head0.237
Teacher spread0.228 · 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; both teacher heads agree on what is shown here.

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

Citations23
Published2005
Admission routes1
Has abstractyes

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