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Record W4242123296 · doi:10.1098/rsos.170351

Speciation over the edge: gene flow among non-human primate species across a formidable biogeographic barrier

2017· article· en· W4242123296 on OpenAlexafffund
Ben J. Evans, Anthony J. Tosi, Kai Zeng, Jonathan Dushoff, André Corvelo, Don J. Melnick

Bibliographic record

VenueRoyal Society Open Science · 2017
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenetic diversity and population structure
Canadian institutionsMcMaster University
FundersNatural Sciences and Engineering Research Council of CanadaLembaga Ilmu Pengetahuan IndonesiaKent State University
KeywordsGene flowGenetic algorithmBiological dispersalMacaqueBiologyEvolutionary biologyVertebrateBiogeographyEcologyGeneGeneticsGenetic variationPopulation

Abstract

fetched live from OpenAlex

Many genera of terrestrial vertebrates diversified exclusively on one or the other side of Wallace's Line, which lies between Borneo and Sulawesi islands in Southeast Asia, and demarcates one of the sharpest biogeographic transition zones in the world. Macaque monkeys are unusual among vertebrate genera in that they are distributed on both sides of Wallace's Line, raising the question of whether dispersal across this barrier was an evolutionary one-off or a more protracted exchange-and if the latter, what were the genomic consequences. To explore the nature of speciation over the edge of this biogeographic divide, we used genomic data to test for evidence of gene flow between macaque species across Wallace's Line after macaques colonized Sulawesi. We recovered evidence of post-colonization gene flow, most prominently on the X chromosome. These results are consistent with the proposal that gene flow is a pervasive component of speciation-even when barriers to gene flow seem almost insurmountable.

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)0.001
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 score0.003
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
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 head0.303
Teacher spread0.289 · 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

Citations24
Published2017
Admission routes2
Has abstractyes

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