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Record W2736570846 · doi:10.1139/gen-2016-0206

Phylogenomics reveals a complex evolutionary history of lobed-leaf white oaks in western North America

2017· article· en· W2736570846 on OpenAlexvenueno aff
John D. McVay, Duncan Hauser, Andrew L. Hipp, Paul S. Manos

Bibliographic record

VenueGenome · 2017
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenetic diversity and population structure
Canadian institutionsnot available
FundersUniversity of OxfordNational Science Foundation
KeywordsBiologyIntrogressionPhylogenomicsSympatric speciationPhylogenetic treeLineage (genetic)Evolutionary biologyCoalescent theoryRange (aeronautics)PhylogeneticsContext (archaeology)PhylogeographyCladeEcologyGenetics

Abstract

fetched live from OpenAlex

Species within the genus Quercus (oak) hybridize in complex patterns that have yet to be fully explored with phylogenomic data. Analyses to date have recovered reasonable divergent patterns, suggesting that the impact of introgression may not always be obvious in inferred oak phylogenies. We explore this phenomenon using RADseq data for 136 samples representing 54 oak species by conducting phylogenetic analyses designed to distinguish signals of lineage diversification and hybridization, focusing on the lobed-leaf species Quercus gambelii, Q. lobata, and Q. garryana in the context of a broad sampling of allied white oaks (Quercus section Quercus), and particularly the midwestern Q. macrocarpa. We demonstrate that historical introgressive hybridization between once sympatric species affects phylogeny estimation. Historical range expansion during periods of favorable climate likely explains our observations; analyses support genetic exchange between ancestral populations of Q. gambelii and Q. macrocarpa. We conclude that the genomic consequences of introgression caused the attraction of distant lineages in phylogenetic tree space, and that introgressive and divergent signals can be disentangled to produce a robust estimate of the phylogenetic history of the species.

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 score0.248
Threshold uncertainty score0.477

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 head0.022
GPT teacher head0.230
Teacher spread0.208 · 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

Citations54
Published2017
Admission routes1
Has abstractyes

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