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Record W4398254150 · doi:10.1111/ppa.13924

The effects of <i>Lr34</i> and <i>Lr67</i> on Fusarium head blight resistance and deoxynivalenol accumulation in wheat

2024· article· en· W4398254150 on OpenAlexafffund
Brent McCallum, Colin W. Hiebert, Curt A. McCartney, María Antonia Henríquez

Bibliographic record

VenuePlant Pathology · 2024
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicMycotoxins in Agriculture and Food
Canadian institutionsUniversity of ManitobaAgriculture and Agri-Food Canada
FundersAgriculture and Agri-Food Canada
KeywordsBiologyFusariumMycotoxinResistance (ecology)Head (geology)AgronomyBlightHorticultureBotany

Abstract

fetched live from OpenAlex

Abstract The resistance gene Lr34 conditions durable disease resistance to many biotrophic wheat pathogens and has been incorporated into many wheat cultivars throughout the world. Lr67 is a similar adult plant resistance gene that also conditions resistance to multiple wheat diseases. Both genes significantly reduce disease severity and work in an additive manner with other rust resistance genes. To determine the effect of Lr34 and Lr67 on Fusarium head blight (FHB), two doubled haploid populations, segregating for each of these genes, were developed in the Thatcher wheat background by crossing near‐isogenic lines. Progeny from these populations were tested in five Fusarium graminearum ‐inoculated disease nurseries and assessed for FHB symptoms, using a visual rating index (VRI), and deoxynivalenol (DON) accumulation. Both Lr34 and Lr67 significantly reduced visual symptoms of FHB and DON overall, though those effects were not significant in all environments. The overall reduction in the group of progeny with the resistant allele compared to the group with the susceptible allele for VRI was 9.4% for Lr34 and 6.8% for Lr67 , while for DON it was 16.4% for Lr34 and 14.9% for Lr67 . These genes represent important tools for improving resistance to FHB and many other diseases in wheat.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.873
Threshold uncertainty score0.127

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 head0.012
GPT teacher head0.226
Teacher spread0.214 · 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 designBench or experimental
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

Citations3
Published2024
Admission routes2
Has abstractyes

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