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Record W2790088472 · doi:10.1094/pdis-12-17-1920-re

Fungicide and Cultivar Management of Leaf Spot Diseases of Winter Wheat in Western Canada

2018· article· en· W2790088472 on OpenAlexaffabout
H. R. Kutcher, T. Kelly Turkington, Debra L. McLaren, R. B. Irvine, Gurcharn S. Brar

Bibliographic record

VenuePlant Disease · 2018
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicWheat and Barley Genetics and Pathology
Canadian institutionsAgriculture and Agri-Food CanadaUniversity of Saskatchewan
Fundersnot available
KeywordsFungicideLeaf spotSeptoriaPowdery mildewBiologyCultivarAgronomyStrobilurinMycosphaerella graminicolaWinter wheatHorticultureYield (engineering)Azoxystrobin

Abstract

fetched live from OpenAlex

A complex of leaf-spotting diseases-tan spot, the Septoria complex, spot blotch, and powdery mildew-are frequently observed on winter wheat in western Canada; however, there are few studies indicating varietal differences in reaction to these diseases or the benefit of fungicide application. To determine the benefit of varietal improvement and multiple fungicide treatments and application timings, field experiments were conducted at six site-years in western Canada. Two cultivars varying in reaction to leaf spot diseases were used in combination with fungicide treatments. Disease severity ranged from trace to 64% of the combined flag and penultimate leaf area diseased, differed between cultivars, and was reduced from the check by some fungicide treatments. Yield improvement by fungicide treatment varied from 3.3 to 13.2% greater than the nontreated check. At two site-years, the split application of two half rates of fungicide resulted in the greatest yield; however, in both cases, the yield benefit did not differ from a single application at the flag leaf growth stage. Cultivar selection and fungicide use under environments conducive to disease are beneficial components of an integrated leaf spot disease management program for winter wheat in western Canada.

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 score0.110
Threshold uncertainty score0.993

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 head0.204
Teacher spread0.191 · 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
Published2018
Admission routes2
Has abstractyes

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