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Record W2964733764 · doi:10.7554/elife.45199

Natural selection and repeated patterns of molecular evolution following allopatric divergence

2019· article· en· W2964733764 on OpenAlexaff
Yibo Dong, Shichao Chen, Shifeng Cheng, Wenbin Zhou, Qing Ma, Zhiduan Chen, Chengxin Fu, Xin Liu, Yunpeng Zhao, Pamela S. Soltis, Gane Ka‐Shu Wong, Qiu‐Yun Xiang

Bibliographic record

VenueeLife · 2019
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenetic diversity and population structure
Canadian institutionsUniversity of Alberta
FundersNational Natural Science Foundation of ChinaNational Science Foundation
KeywordsAllopatric speciationNegative selectionBiologyEvolutionary biologyDivergence (linguistics)CladeNatural selectionSelection (genetic algorithm)Genetic algorithmAbundance (ecology)GeneticsGenomePhylogeneticsGeneEcologyPopulation

Abstract

fetched live from OpenAlex

Although geographic isolation is a leading driver of speciation, the tempo and pattern of divergence at the genomic level remain unclear. We examine genome-wide divergence of putatively single-copy orthologous genes (POGs) in 20 allopatric species/variety pairs from diverse angiosperm clades, with 16 pairs reflecting the classic eastern Asia-eastern North America floristic disjunction. In each pair, >90% of POGs are under purifying selection, and <10% are under positive selection. A set of POGs are under strong positive selection, 14 of which are shared by 10-15 pairs, and one shared by all pairs; 15 POGs are annotated to biological processes responding to various stimuli. The relative abundance of POGs under different selective forces exhibits a repeated pattern among pairs despite an ~10 million-year difference in divergence time. Species divergence times are positively correlated with abundance of POGs under moderate purifying selection, but negatively correlated with abundance of POGs under strong purifying selection.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.230
Threshold uncertainty score0.255

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 head0.003
GPT teacher head0.203
Teacher spread0.200 · 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

Citations26
Published2019
Admission routes1
Has abstractyes

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