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Record W4407189659 · doi:10.1080/07060661.2024.2448690

Current understanding and future perspectives on pathogen biology and management of potato and tomato late blight ( <i>Phytophthora infestans</i> ) in Canada

2025· article· en· W4407189659 on OpenAlexaffvenueabout
Segun Babarinde, Khalil I. Al-Mughrabi, Rishi R. Burlakoti, R. D. Peters, Samuel K. Asiedu, Balakrishnan Prithiviraj

Bibliographic record

VenueCanadian Journal of Plant Pathology · 2025
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant Pathogens and Resistance
Canadian institutionsAgriculture and Agri-Food CanadaDalhousie University
Fundersnot available
KeywordsPhytophthora infestansBlightBiologyPathogenHorticultureMicrobiology

Abstract

fetched live from OpenAlex

Biotic and abiotic factors such as drought, pests, and diseases limit the quality and yield of potato and tomato worldwide. Late blight is caused by the itinerant oomycete pathogen Phytophthora infestans (Mont.) de Bary, the main cause of the Great Irish Famine in the mid-nineteenth century. In recent years, changes in the distribution of P. infestans strains have been observed worldwide, including in Canada. Genetic diversity of P. infestans populations has increased in Canada in recent decades and major shifts in genotypes have occurred over time. The shift in pathogen genotypes was often associated with their diversity in aggressiveness/virulence, pathogenicity and fungicide sensitivity. The appearance of novel genotypes of P. infestans was also reported from several provinces of Canada; however, there is a need for further research to understand the mechanism of evolution of these novel strains and the impact of these strains on late blight management. In this manuscript, we offer an in-depth review of late blight in Canada, including pathogen identification and characterization tools, temporal genotypic diversity of P. infestans strains, the pathogen’s profile for fungicide sensitivity, and host–pathogen interactions. Furthermore, we critically review several management strategies in Canada, such as the use of host resistance, chemical treatments, imaging and forecasting technologies, cultural practices, and biological control methods for managing late blight. Lastly, we describe knowledge gaps and future perspectives for the effective management of late blight within the current global and technological context.

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 score0.845
Threshold uncertainty score0.985

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 head0.205
Teacher spread0.193 · 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

Citations2
Published2025
Admission routes3
Has abstractyes

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