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Record W2774067396 · doi:10.1038/s41598-017-17569-7

Multiple gene genealogy reveals high genetic diversity and evidence for multiple origins of Chinese Plasmopara viticola population

2017· article· en· W2774067396 on OpenAlexaff
Wei Zhang, Ishara S. Manawasinghe, Wensheng Zhao, Jianping Xu, Siraprapa Brooks, Xueyan Zhao, Kevin D. Hyde, K. W. Thilini Chethana, Jianhua Liu, Xinghong Li, Jiye Yan

Bibliographic record

VenueScientific Reports · 2017
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicHorticultural and Viticultural Research
Canadian institutionsMcMaster University
Fundersnot available
KeywordsPlasmopara viticolaBiologyHaplotypeGeneticsPhylogenetic treePopulationGenetic diversityDowny mildewGeneEvolutionary biologyBotanyGenotypeMedicine

Abstract

fetched live from OpenAlex

Downy mildew caused by Plasmopara viticola is one of the most devastating diseases of grapevines worldwide. So far, the genetic diversity and origin of the Chinese P. viticola population are unclear. In the present study, 103 P. viticola isolates were sequenced at four gene regions: internal transcribed spacer one (ITS), large subunit of ribosomal RNA (LSU), actin gene (ACT) and beta-tubulin (TUB). The sequences were analyzed to obtain polymorphism and diversity information of the Chinese population as well as to infer the relationships between Chinese and American isolates. High genetic diversity was observed for the Chinese population, with evidence of sub-structuring based on climate. Phylogenetic analysis and haplotype networks showed evidence of close relationships between some American and Chinese isolates, consistent with recent introduction from America to China via planting materials. However, there is also evidence for endemic Chinese P. viticola isolates. Our results suggest that the current Chinese Plasmopara viticola population is an admixture of endemic and introduced isolates.

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)0.000
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 score0.009
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.085
GPT teacher head0.321
Teacher spread0.235 · 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 routes1
Has abstractyes

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