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Record W4386145654 · doi:10.22543/0090-0222.2077

Gene Flow Between Great Lakes Region Populations of the Canadian Tiger Swallowtail Butterfly, <i>Papilio Canadensis</i>, Near the Hybrid Zone With <i>P. Glaucus</i> (Lepidoptera: Papilionidae)

2018· article· en· W4386145654 on OpenAlexafffundabout
Aram D. Stump, Felix A. H. Sperling, Amber Crim, J. Mark Scriber

Bibliographic record

VenueThe Great Lakes Entomologist · 2018
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicLepidoptera: Biology and Taxonomy
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of CanadaCollege of Engineering, Michigan State UniversityMichigan State UniversityNational Science Foundation
KeywordsBiologyGene flowIntrogressionHybrid zonePopulationPopulation geneticsMitochondrial DNABotanyZoologyGenetic variationGeneGenetics

Abstract

fetched live from OpenAlex

Papilio canadensis were sampled from three locations on either side of Lake Michigan to study gene flow near and through a butterfly hybrid zone. Allele frequencies at four polymorphic enzyme loci, as indicated by allozyme electrophoresis, were similar in all samples. Values for FST were close to zero, indicating that gene flow is high among these populations, even when separated by Lake Michigan. We developed a mitochondrial DNA marker with diagnostic differences between P. canadensis and its parapatric sister species Papilio glaucus, based on PCR-RFLP. P. glaucus haplotypes of this mtDNA marker and P. glaucus alleles of a diagnostic allozyme locus (PGD) were found in P. canadensis populations sampled in Michigan’s Lower Peninsula but not in the Upper Peninsula or Northern Minnesota. The presence of P. glaucus alleles in P. canadensis populations could be due to introgression through hybridization, or could be remnants of a P. glaucus population that was inundated by an influx of P. canadensis alleles.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesScience and technology studies
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.728
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0020.005
Scholarly communication0.0000.000
Open science0.0020.000
Research integrity0.0010.000
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 head0.021
GPT teacher head0.226
Teacher spread0.205 · 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 designNot applicable
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

Citations1
Published2018
Admission routes3
Has abstractyes

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