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Record W3042859792 · doi:10.1002/ece3.6523

Ancient drainage networks mediated a large‐scale genetic introgression in the East Asian freshwater snails

2020· article· en· W3042859792 on OpenAlexfundno aff
Osamu Miura, Misako Urabe, Hideaki Mori, Satoshi Chiba

Bibliographic record

VenueEcology and Evolution · 2020
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenetic diversity and population structure
Canadian institutionsnot available
FundersJapan Society for the Promotion of ScienceStrongGénome QuébecMcGill University
KeywordsIntrogressionBiologyMitochondrial DNAPhylogeographyEcologyEndemismPleistoceneBiogeographyPaleontologyPhylogeneticsGene

Abstract

fetched live from OpenAlex

Abstract Biogeography and genetic variation of freshwater organisms are influenced not only by current freshwater connections but also by past drainage networks. The Seto Inland Sea is a shallow enclosed sea in Japan, but geological evidence showed that a large freshwater drainage had intermittently appeared in this area between the late Pliocene and Pleistocene. Here, we demonstrated that this paleodrainage greatly affected the genetic variation of the East Asian freshwater snails, Semisulcospira spp. We found that the mtDNA haplotypes originated in the Lake Biwa endemic Semisulcospira species at the upstream side of the paleodrainage were frequently observed in the riverine Semisulcospira species at its downstream side. The genome‐wide DNA and morphological analyses consistently showed that there was no clear evidence of nuclear introgression between the Lake Biwa endemics and riverine species. These results suggest that the large paleodrainage had facilitated mitochondrial introgression and had broadly spread the introgressed mtDNA haplotypes to its downstream region around the Seto Inland Sea. Our study highlights the role of paleodrainages in shaping the genetic variation of freshwater organisms.

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 score0.031
Threshold uncertainty score0.264

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 head0.006
GPT teacher head0.200
Teacher spread0.194 · 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

Citations27
Published2020
Admission routes1
Has abstractyes

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