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Record W2077757349 · doi:10.1525/cond.2008.8613

VARIABLE WEATHER PATTERNS AFFECT ANNUAL SURVIVAL OF NORTHERN FLICKERS MORE THAN PHENOTYPE IN THE HYBRID ZONE

2008· article· en· W2077757349 on OpenAlexafffund
D. T. Tyler Flockhart, Karen L. Wiebe

Bibliographic record

VenueOrnithological Applications · 2008
Typearticle
Languageen
FieldEnvironmental Science
TopicFish Ecology and Management Studies
Canadian institutionsUniversity of Saskatchewan
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsBiologyTraitEcologyHybrid zoneHybridPredationDemographyGeneticsGeneGenetic variation

Abstract

fetched live from OpenAlex

Fitness in a hybrid zone is determined both by the reproductive success and the survival of phenotypes. The hybrid zone of Northern Flickers (Colaptes auratus), a common woodpecker, is one of the largest and most well-known hybrid zones in North America. Bounded-hybrid superiority, the most widely accepted hypothesis for the persistence of the zone, suggests hybrids should have equal or higher reproduction or survival than parental types in the zone, but the latter life history trait has never been examined. We analyzed the apparent survival of 1117 flickers over nine years using capture-recapture models and found no evidence that the phenotypic hybrid index influenced survival. Instead, annual adult apparent survival was best modeled according to large-scale weather patterns such as the North Atlantic Oscillation (NAO). During warm phases of the NAO, adult flickers had lower survival compared to cooler phases of NAO. There was no evidence that phenotype influenced the local recruitment of yearling flickers to the study area and no effect of NAO on this relationship. These results suggest survival in the flicker hybrid zone is largely influenced by annually variable weather patterns and that if there are differences in survival according to phenotype, they are extremely small for the flicker hybrid zone in the north.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.029
Threshold uncertainty score0.727

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 head0.014
GPT teacher head0.230
Teacher spread0.215 · 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

Citations12
Published2008
Admission routes2
Has abstractyes

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