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Record W2058975254 · doi:10.1094/phyto-03-14-0065-r

Genetic Analysis of Leaf Rust Resistance in Six Durum Wheat Genotypes

2014· article· en· W2058975254 on OpenAlexaff
Alexander Loladze, Dhouha Kthiri, Curtis Pozniak, Karim Ammar

Bibliographic record

VenuePhytopathology · 2014
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicWheat and Barley Genetics and Pathology
Canadian institutionsUniversity of Saskatchewan
FundersBorlaug Global Rust Initiative
KeywordsBiologyRust (programming language)SeedlingGenotypeResistance (ecology)AgronomyGeneQuantitative trait locusAlleleGenetic analysisPoaceaeHorticultureGenetics

Abstract

fetched live from OpenAlex

Leaf rust, caused by Puccinia triticina, is one of the main fungal diseases limiting durum wheat production. This study aimed to characterize previously undescribed genes for leaf rust resistance in durum wheat. Six different resistant durum genotypes were crossed to two susceptible International Maize and Wheat Improvement Center (CIMMYT) lines and the resulting F1, F2, and F3 progenies were evaluated for leaf rust reactions in the field and under greenhouse conditions. In addition, allelism tests were conducted. The results of the study indicated that most genotypes carried single effective dominant or recessive seedling resistance genes; the only exception to this was genotype Gaza, which carried one adult plant and one seedling resistance gene. In addition, it was concluded that the resistance genes identified in the current study were neither allelic to LrCamayo or Lr61, nor were they related to Lr3 or Lr14a, the genes that already are either ineffective or are considered to be vulnerable for breeding purposes. A complicated allelic or linkage relationship between the identified genes is discussed. The results of the study will be useful for breeding for durable resistance by creating polygenic complexes.

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 score0.539
Threshold uncertainty score0.804

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.012
GPT teacher head0.214
Teacher spread0.202 · 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

Citations19
Published2014
Admission routes1
Has abstractyes

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