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Record W2027303766 · doi:10.1139/g07-090

SSR marker diversity of soybean aphid resistance sources in North America

2007· article· en· W2027303766 on OpenAlexvenueno aff
Charles Y. Chen, Cuihua Gu, Clarice Mensah, Randall L. Nelson, Dechun Wang

Bibliographic record

VenueGenome · 2007
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicInsect-Plant Interactions and Control
Canadian institutionsnot available
Fundersnot available
KeywordsBiologyAphidGenetic diversityPEST analysisGeneticsSoybean aphidPedigree chartAlleleAnalysis of molecular varianceCultivarAphididaeMicrosatelliteGeneBotanyHomopteraPopulationDemography

Abstract

fetched live from OpenAlex

The soybean aphid (Aphis glycines Matsumura) has become a major pest of soybean in North America since 2000. Seven aphid resistance sources, PI 71506, Dowling, Jackson, PI 567541B, PI 567598B, PI 567543C, and PI 567597C, have been identified. Knowledge of genetic relationships among these sources and their ancestral parents will help breeders develop new cultivars with different resistance genes. The objective of this research was to examine the genetic relationships among these resistance sources. Sixty-one lines were tested with 86 simple sequence repeat (SSR) markers from 20 linkage groups. Non-hierarchical (VARCLUS) and hierarchical (Ward's) clustering and multidimensional scaling (MDS) were used to determine relationships among the 61 lines. Two hundred and sixty-two alleles of the 86 SSR loci were detected with a mean polymorphism information content of 0.36. The 61 lines were grouped into 4 clusters by both clustering methods and the MDS results consistently corresponded to the assigned clusters. The 7 resistance sources were clustered into 3 different groups corresponding to their geographical origins and known pedigree information, indicating genetic differences among these sources. The largest variation was found among individuals within different clusters by analysis of molecular variance.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.317
Threshold uncertainty score0.992

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 head0.009
GPT teacher head0.182
Teacher spread0.173 · 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

Citations10
Published2007
Admission routes1
Has abstractyes

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