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Record W4389420568 · doi:10.1080/07060661.2023.2287164

First report of yeast spot caused by <i>Eremothecium coryli</i> on soybean in South Korea

2023· article· en· W4389420568 on OpenAlexvenueno aff
Okhee Choi, Haeun Noh, Yongsung Kang, Hyun Jin Chun, Min Chul Kim, Jinwoo Kim

Bibliographic record

VenueCanadian Journal of Plant Pathology · 2023
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicPlant Pathogens and Fungal Diseases
Canadian institutionsnot available
FundersNational Research Foundation of KoreaMinistry of Education
KeywordsYeastLeaf spotBiologyBotanyGenetics

Abstract

fetched live from OpenAlex

An ascomycete yeast was isolated from tissue cultures of soybean explants (cv. Williams 82) exhibiting blanching and discoloration. Stink bug damage was confirmed in soybean seeds that were grown and harvested simultaneously in the same field as the soybeans used for tissue culture, and the same yeast was isolated from areas of stink bug damage, indicating seed contamination. The seed infection rate was 20%, and all infected seeds had been damaged by stink bugs. The yeast was identified as Eremothecium coryli (Peglion) Kurtzman based on morphological and biochemical characteristics and homology analysis of the internal transcribed spacer sequence of the ribosomal RNA gene and the large ribosomal subunit D1/D2 domain regions. Disease symptoms were reproduced by inoculating this yeast into healthy soybeans (cv. Williams 82). Eremothecium coryli has been reported to cause yeast spot disease in soybeans, but has not been reported in Korea. Among the fungicides recommended for plant tissue culture, we recommend imazalil for addressing E. coryli contamination. The results of this study suggest that yeast spot caused by E. coryli presents a significant risk to soybean production.

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: Case report · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.013
GPT teacher head0.207
Teacher spread0.195 · 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 designCase report
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

Citations2
Published2023
Admission routes1
Has abstractyes

Explore more

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