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Record W4317207925 · doi:10.3390/plants12030434

Blackleg Yield Losses and Interactions with Verticillium Stripe in Canola (Brassica napus) in Canada

2023· article· en· W4317207925 on OpenAlexafffundabout
Yixiao Wang, Stephen E. Strelkov, Sheau‐Fang Hwang

Bibliographic record

VenuePlants · 2023
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant Disease Resistance and Genetics
Canadian institutionsUniversity of Alberta
FundersAlberta Canola Producers CommissionCanola Council of CanadaUniversity of AlbertaSaskatchewan Canola Development Commission
KeywordsBlacklegLeptosphaeria maculansCanolaBiologyBrassicaVerticilliumAgronomyHybridCankerYield (engineering)Horticulture

Abstract

fetched live from OpenAlex

Blackleg, caused by Leptosphaeria maculans, is an important disease of canola (Brassica napus). The pathogen can attack stems, leaves and pods, but basal stem cankers are most damaging and can result in significant yield losses. In Canada, Verticillium stripe (Verticillium longisporum) has recently emerged as another disease threat to canola. Symptoms of Verticillium stripe can resemble those of blackleg, and the two diseases may occur together. The effect of blackleg on yield was explored in field experiments with two canola hybrids and by evaluating a wider variety of hybrids in commercial crops in central Alberta, Canada. The impact on yield of L. maculans/V. longisporum interactions was also assessed under field and greenhouse conditions. In most hybrids, the relationship between blackleg severity and yield components was best explained by second-degree quadratic equations, although a linear relationship was found for one variety sampled in commercial fields. When L. maculans was co-inoculated with V. longisporum, blackleg severity and yield losses increased. In some cases, Verticillium stripe caused greater yield losses than blackleg. The results suggest that the interaction between L. maculans/V. longisporum may cause more severe losses in canola, highlighting the need for proactive disease management strategies.

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 score0.475
Threshold uncertainty score0.481

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 head0.018
GPT teacher head0.195
Teacher spread0.176 · 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

Citations20
Published2023
Admission routes3
Has abstractyes

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