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Record W2314822851 · doi:10.2135/cropsci2013.09.0649

Biotypic Variation among Soybean Aphid Isolates from Four Provinces in China

2014· article· en· W2314822851 on OpenAlexfundno aff
Yun Zhong, Xiao Liang, Biao Wang, Yi N. Jiang, Jun Yan, Lin Cheng, Tian Long Wu

Bibliographic record

VenueCrop Science · 2014
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicInsect-Plant Interactions and Control
Canadian institutionsnot available
FundersNational Natural Science Foundation of ChinaMinistry of Agriculture - Saskatchewan
KeywordsSoybean aphidBiologyAphididaeAphidPEST analysisHemipteraAgronomyHomopteraChinaHorticultureBotanyGeography

Abstract

fetched live from OpenAlex

ABSTRACT The soybean aphid [Aphis glycines Matsumura (Hemiptera: Aphididae)] is an important pest insect of soybean [Glycine max (L.) Merr.] in China and North America. Identification of soybean aphid biotypes would facilitate more effective pest management. Although four soybean aphid biotypes have been discovered in North America to date, biotype determination has not been conducted in China. In this study, we analyzed the biotypes of soybean aphids collected from four different soybean‐growing areas in China in 2011. The responses of four soybean aphid isolates from Changchun (Jilin province), Jinan (Shandong province), and Shanghai and Guiyang (Guizhou province) were assessed by infesting 11 soybean genotypes in noncaged tests during 2 yr and infesting 7 genotypes in a caged test in 1 yr. We distinguished among the four isolates on the basis of their abilities to infest a set of host plants with the known resistance genes Rag1 through Rag6. The results showed that the biotype profiles of the four soybean aphid isolates were different from each other and that there are at least four biotypes of soybean aphid in China.

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 score0.033
Threshold uncertainty score0.065

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)0.0000.000

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 head0.007
GPT teacher head0.194
Teacher spread0.186 · 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

Citations6
Published2014
Admission routes1
Has abstractyes

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