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Record W2014504160 · doi:10.1094/pd-90-0314

Heritability of Resistance to Verticillium Wilt in Alfalfa

2006· article· en· W2014504160 on OpenAlexaboutno aff
George J. Vandemark, R. C. Larsen, Teresa J. Hughes

Bibliographic record

VenuePlant Disease · 2006
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant pathogens and resistance mechanisms
Canadian institutionsnot available
FundersU.S. Department of Agriculture
KeywordsVerticillium wiltHeritabilityBiologyCultivarVerticilliumVerticillium dahliaePlant disease resistanceVeterinary medicineHost resistanceResistance (ecology)Restricted maximum likelihoodGenetic correlationAgronomyHorticultureGenetic variationGeneticsMaximum likelihoodStatisticsGene

Abstract

fetched live from OpenAlex

Verticillium wilt of alfalfa, caused by Verticillium albo-atrum, may reduce forage yields by up to 50% in alfalfa-producing areas of the northern United States and Canada. It has been suggested that cultivars require at least 60% resistant plants to afford maximum protection against disease. Our objective was to calculate heritability estimates of resistance to Verticillium wilt in alfalfa. Estimates were generated for two alfalfa populations developed from the cvs. Affinity + Z and Depend + EV. Heritability on a half-sib progeny means basis was calculated based on data from greenhouse pathogenicity tests. Estimates based on repeated experiments conducted for single years (2004 and 2005) were high for both populations, ranging from 0.86 to 0.92. The heritability estimate based on data collected over 2 years was 0.26 for Affinity + Z and 0.66 for Depend + EV. Disease was more severe in 2005 than in 2004. However, the Spearman rank correlation between mean disease severity index values for half-sib families over 2 years was positive and significant for both populations. Results of pathogenicity tests suggested that neither cultivar had resistance levels approaching 60%. The heritability estimates suggest that resistance levels in both Affinity + Z and Depend + EV could be improved further through selection.

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 score0.933
Threshold uncertainty score0.742

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 head0.010
GPT teacher head0.181
Teacher spread0.171 · 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

Citations9
Published2006
Admission routes1
Has abstractyes

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