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Record W2112947203 · doi:10.1111/ele.12326

Hybrid songbirds employ intermediate routes in a migratory divide

2014· article· en· W2112947203 on OpenAlexafffundabout
Kira E. Delmore, Darren E. Irwin

Bibliographic record

VenueEcology Letters · 2014
Typearticle
Languageen
FieldEnvironmental Science
TopicAvian ecology and behavior
Canadian institutionsUniversity of British Columbia
FundersBritish Antarctic SurveyNatural Sciences and Engineering Research Council of CanadaUniversity of British ColumbiaSociety of Canadian OrnithologistsSociety for the Study of EvolutionAmerican Ornithologists' Union
KeywordsHybridHybrid zoneBird migrationSelection (genetic algorithm)EcologyBiologyAridGenetic algorithmGeographyGenetic variationGenetics

Abstract

fetched live from OpenAlex

Migratory divides are contact zones between populations that use different routes to navigate around unsuitable areas on seasonal migration. Hybrids in divides have been predicted to employ intermediate and potentially inferior routes. We provide the first direct test of this hypothesis, using light-level geolocators to track birds breeding in a hybrid zone between Swainson's thrushes in western Canada. Compared to parental forms, hybrids exhibited increased variability in their migratory routes, with some using intermediate routes that crossed arid and mountainous regions, and some using the same routes as one parental group on fall migration and the other on spring migration. Hybrids also tended to use geographically intermediate wintering sites. Analysis of genetic variation across the hybrid zone suggests moderately strong selection against hybrids. These results indicate that seasonal migratory behaviour might be a source of selection against hybrids, supporting a possible role for migration in speciation.

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)0.000
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 score0.006
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.000
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 head0.005
GPT teacher head0.203
Teacher spread0.198 · 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

Citations175
Published2014
Admission routes3
Has abstractyes

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