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Record W3004652939 · doi:10.1139/cjb-2019-0163

Screening of lupine germplasm for resistance against <i>Phytophthora sojae</i>

2020· article· en· W3004652939 on OpenAlexvenueno aff
Gayathri Beligala, Helen J. Michaels, Vipaporn Phuntumart

Bibliographic record

VenueBotany · 2020
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant Pathogens and Resistance
Canadian institutionsnot available
FundersNational Institute of Food and AgricultureU.S. Department of Agriculture
KeywordsPhytophthora sojaeBiologyGermplasmPhytophthoraZoosporeRoot rotBotanyResistance (ecology)PathogenAgronomyMicrobiologySpore

Abstract

fetched live from OpenAlex

Phytophthora sojae is a major pathogen in cultivated soybeans world-wide. Although incorporating resistance genes has been an effective management tool for soybean breeders, surveys of soybean fields in the Midwest US indicate that some P. sojae strains are capable of overcoming all known resistance genes. While P. sojae is known to have a very narrow host range, it can also infect Lupinus (lupine), varieties of which may provide potential sources for novel resistance genes that can be genetically engineered into soybean. The chemotactic behavior of zoospores and pathogenicity of P. sojae strain P6497 towards 17 lupine lines were explored. The two soybean varieties Williams and Williams 82 that are susceptible and resistant against P. sojae P6497, respectively, were used as controls. Chemotaxis assays showed that there was no coherent pattern between the number of zoospores colonizing the root surface and plant tolerance or resistance to phytophthora root rot. Pathogenicity tests identified that two of the 17 lupine lines tested (LAB 18 and LL 35) were resistant to P. sojae infection. Phylogenetic analysis of these two resistant lupine lines with Old World lupines of the Mediterranean and North African regions, and New World lupines of America, indicated that they originated from the Old World.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.962
Threshold uncertainty score0.172

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 head0.031
GPT teacher head0.218
Teacher spread0.187 · 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 designBench or experimental
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

Citations1
Published2020
Admission routes1
Has abstractyes

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