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Record W4200457308 · doi:10.1080/07060661.2021.2011420

Understanding the root rot and wilting complex of raspberry: current research advances and future perspectives

2021· article· en· W4200457308 on OpenAlexaffvenueabout
Sanjib Sapkota, Rishi R. Burlakoti, Zamir K. Punja, Michael Dossett, Eric M. Gerbrandt

Bibliographic record

VenueCanadian Journal of Plant Pathology · 2021
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant Pathogens and Resistance
Canadian institutionsBritish Columbia Blueberry CouncilWSP (Canada)Simon Fraser UniversityAgriculture and Agri-Food Canada
Fundersnot available
KeywordsBiologyWiltingBlowing a raspberryRubusRoot rotPhytophthoraPlant disease resistanceDisease managementResistance (ecology)Biological dispersalCropAgronomyBotanyHorticulture

Abstract

fetched live from OpenAlex

Red raspberry (Rubus idaeus L.) is an important fruit crop in British Columbia, Canada, and the Pacific Northwest (PNW) region of the USA, as well as in other regions of the world. Root rot and wilting complex (RRWC), primarily caused by Phytophthora rubi, is the most important biotic constraint responsible for declining raspberry production in these regions, causing millions of dollars in losses. Other root-infecting fungal species and the root lesion nematode (Pratylenchus penetrans) may also be found associated with the disease complex. The average lifespan of raspberry plantings in the PNW is 10 to 12 years, which is reduced to 5 years by the disease complex. Phytophthora spp. play a predominant role in the RRWC complex due to the persistent nature of oospores, rapid dispersal of inoculum, and the polycyclic nature of infection, all of which increase disease severity. In this review, we discuss the current understanding of Phytophthora spp. and other pathogens associated with the RRWC, including pathogen biology and the disease cycle, the impact of infection on the plant, as well as current and potential cultural, biological, and chemical options for management. In addition, we discuss breeding efforts for disease resistance, including conventional and molecular approaches to identify sources of resistance, molecular markers linked to potential resistance genes, and their incorporation into elite breeding materials or cultivars. We also present the current gaps in knowledge, unique challenges, and future perspectives in sustainable disease management of this important disease complex.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.714
Threshold uncertainty score0.955

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.130
GPT teacher head0.288
Teacher spread0.157 · 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

Citations14
Published2021
Admission routes3
Has abstractyes

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