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Record W1963774064 · doi:10.1139/g06-057

Molecular marker diversity of SCN-resistant sources in soybean

2006· article· en· W1963774064 on OpenAlexvenueno aff
Yiwu Chen, Dechun Wang, Prakash R. Arelli, Mohsen Ebrahimi, Randall L. Nelson

Bibliographic record

VenueGenome · 2006
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicNematode management and characterization studies
Canadian institutionsnot available
Fundersnot available
KeywordsSoybean cyst nematodeBiologyGermplasmGenetic diversityAnalysis of molecular varianceGeneticsGenetic variationHierarchical clusteringGenotypeGenetic distanceEvolutionary biologyGeneBotanyGenetic structureCluster analysisPopulation

Abstract

fetched live from OpenAlex

Soybean cyst nematode (SCN) (Heterodera glycines Ichinohe; HG) is one of the most destructive pests of soybean (Glycine max (L.) Merr.) in the United States. Over 100 SCN-resistant accessions within the USDA Soybean Germplasm Collection have been identified, but little is known about the genetic diversity of this SCN-resistant germplasm. The objective of this research was to evaluate the genetic variation and determine the genetic relationships among SCN-resistant accessions. One hundred twenty-two genotypes were evaluated by 85 simple sequence repeat (SSR) markers from 20 linkage groups. Non-hierarchical (VARCLUS) and hierarchical (Ward's) clustering were combined with multidimensional scaling (MDS) to determine relationships among tested lines. The 85 SSR markers produced 566 allelic fragments with a mean polymorphic information content (PIC) value of 0.35. The 122 lines were grouped into 7 clusters by 2 different clustering methods and the MDS results consistently corresponded to the assigned clusters. Assigned clusters were dominated by genotypes that possess one or more unique SCN resistance genes and were associated with geographical origins. The results of analysis of molecular variance (AMOVA) showed that the variation differences among clusters and individual lines were significant, but the differences among individuals within clusters were not significant.

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 score0.849
Threshold uncertainty score0.171

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 head0.011
GPT teacher head0.177
Teacher spread0.165 · 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

Citations33
Published2006
Admission routes1
Has abstractyes

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