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Record W2969961271 · doi:10.1094/pdis-04-19-0866-fe

Virulence Dynamics and Breeding for Resistance to Stripe, Stem, and Leaf Rust in Canada Since 2000

2019· article· en· W2969961271 on OpenAlexaffabout
Gurcharn S. Brar, T. G. Fetch, Brent McCallum, Pierre Hucl, H. R. Kutcher

Bibliographic record

VenuePlant Disease · 2019
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicWheat and Barley Genetics and Pathology
Canadian institutionsAgriculture and Agri-Food CanadaUniversity of Saskatchewan
Fundersnot available
KeywordsBiologyCultivarTriticaleContext (archaeology)Abiotic componentAgronomyCropFrost (temperature)Resistance (ecology)AgriculturePlant disease resistanceGeographyEcology

Abstract

fetched live from OpenAlex

spp.) is a major field crop in Canada in terms of acreage, annual production, and export market value. There are nine classes of Canadian wheat based on growth habit (winter or spring), kernel hardness (hard or soft), seed coat color (red or white), and quality factors (grain protein content and gluten strength). Wheat was described by Newman in 1928 as "the economic fairy to the industrial and commercial life of Canada, having built practically the whole economic structure of the Prairie Provinces." Wheat production in Canada is affected by several biotic and abiotic stresses. The major abiotic stresses are frost damage, drought, and heat stress. Among biotic stresses, diseases caused by fungal pathogens are the most important although wheat streak mosaic virus (WSMV) has caused some localized outbreaks in some years. In context of cultivar registration in Canada, there are certain diseases that breeders have to take into account while developing resistant cultivars. The Prairie Recommending Committee for Wheat, Rye, and Triticale (PRCWRT) classify wheat diseases into priority one, priority two, and priority three depending on prevalence and potential damage they can cause. However, priority one diseases are more of a concern and a minimum level of resistance in commercial cultivars is recommended for those.

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 score0.788
Threshold uncertainty score0.965

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 head0.180
Teacher spread0.170 · 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

Citations43
Published2019
Admission routes2
Has abstractyes

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