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Record W2911027279 · doi:10.1139/cjps-2018-0211

Validation of SNP markers associated with ascochyta blight resistance in pea

2019· article· en· W2911027279 on OpenAlexafffundvenueabout
Ambuj Bhushan Jha, Krishna Kishore Gali, Sabine Banniza, Thomas D. Warkentin

Bibliographic record

VenueCanadian Journal of Plant Science · 2019
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicGenetic and Environmental Crop Studies
Canadian institutionsUniversity of Saskatchewan
FundersSaskatchewan Pulse GrowersWestern Grains Research FoundationMinistry of Agriculture - Saskatchewan
KeywordsAscochytaBlightBiologySingle-nucleotide polymorphismSNPCultivarPlant disease resistanceAgronomyGeneticsGenotypeGene

Abstract

fetched live from OpenAlex

Ascochyta blight of pea is an important disease that can cause severe yield loss. Our previous studies identified several closely linked single nucleotide polymorphism (SNP) markers associated with ascochyta blight resistance. The objective of this study was to validate SNP markers in 36 cultivars from the Saskatchewan pea regional variety trial. Ascochyta blight scores ranged from 1.0 to 9.0 at the physiological maturity stage under field conditions across the 25 site–years in Saskatchewan from 2013 to 2017. Based on Kompetitive Allele-Specific PCR assays, six SNP markers were used for an association study. SNP markers RGA-G3Ap103, PsC8780p118, and PsC22609p103 were significantly (P < 0.05) associated with ascochyta blight scores in 2013 and 2016 at Saskatoon. PsC8780p118 was significantly associated with ascochyta blight scores at Milden in 2014 and Rosthern in 2017. Furthermore, RGA-G3Ap103 showed significant association at Milden in 2014. Based on association studies, RGA-G3Ap103 and PsC8780p118 may have some potential as markers for pea breeding.

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 score0.100
Threshold uncertainty score0.952

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 head0.166
Teacher spread0.155 · 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

Citations16
Published2019
Admission routes4
Has abstractyes

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