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Record W2037263956 · doi:10.1094/pdis-08-14-0863-re

Resistance to <i>Plasmodiophora brassicae</i> in <i>Brassica rapa</i> and <i>Brassica juncea</i> genotypes From China

2014· article· en· W2037263956 on OpenAlexaff
Hui Zhang, Jie Feng, Shujiang Zhang, Shifan Zhang, Fei Li, Stephen E. Strelkov, Rifei Sun, Sheau‐Fang Hwang

Bibliographic record

VenuePlant Disease · 2014
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant Disease Resistance and Genetics
Canadian institutionsAgriculture Food and Rural DevelopmentUniversity of Alberta
Fundersnot available
KeywordsClubrootBrassica rapaBrassicaBiologyCultivarGenotypeHorticultureBotanyGeneGenetics

Abstract

fetched live from OpenAlex

Clubroot disease, caused by Plasmodiophora brassicae Woronin, has become a major problem in cruciferous crops worldwide. Chinese cabbage (Brassica rapa), pak choi (B. rapa), and mustard (B. juncea) are important vegetable crops in China. Development of clubroot-resistant cultivars of these crops is urgently needed. In this study, 71 B. rapa and B. juncea genotypes from China, including cultivars and inbred lines, were evaluated for resistance to three P. brassicae pathotypes. A significant interaction was observed between the P. brassicae pathotypes and the Brassica genotypes. Pathotype 3, as defined on the differentials of Williams, exhibited the weakest virulence on all plant material. By contrast, pathotypes 5 and 6 were both highly pathogenic on most of the tested genotypes. In all, 10 of the 14 Chinese cabbage cultivars were resistant to all three pathotypes, while 4 were resistant only to a specific pathotype. Seven of eight progenies obtained from the selfing of Chinese cabbage cultivars were resistant to pathotype 3 but most were susceptible to pathotypes 5 and 6. Most inbred lines of Chinese cabbage and all inbred lines of pak choi and mustard were susceptible to all three pathotypes but their susceptibility was lower to pathotype 3 than to pathotypes 5 and 6.

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 score0.571
Threshold uncertainty score0.648

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 head0.005
GPT teacher head0.168
Teacher spread0.163 · 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

Citations42
Published2014
Admission routes1
Has abstractyes

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