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Record W2608161400 · doi:10.1590/0100-29452017411

GRAPEVINE VIRUS DISEASES:ECONOMIC IMPACT AND CURRENT ADVANCES IN VIRAL PROSPECTION AND MANAGEMENT

2017· article· en· W2608161400 on OpenAlexaff
Marcos Fernando Basso, T. V. M. Fajardo, Pasquale Saldarelli

Bibliographic record

VenueRevista Brasileira de Fruticultura · 2017
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant Virus Research Studies
Canadian institutionsPlant Biotechnology Institute
Fundersnot available
KeywordsBiologyVirus diseasesCropPerennial plantVirusAgronomyVirology

Abstract

fetched live from OpenAlex

ABSTRACT Grapevine (Vitis spp.) is a major vegetative propagated fruit crop with high socioeconomic importance worldwide. It is susceptible to several graft-transmitted agents that cause several diseases and substantial crop losses, reducing fruit quality and plant vigor, and shorten the longevity of vines. The vegetative propagation and frequent exchanges of propagative material among countries contribute to spread these pathogens, favoring the emergence of complex diseases. Its perennial life cycle further accelerates the mixing and introduction of several viral agents into a single plant. Currently, approximately 65 viruses belonging to different families have been reported infecting grapevines, but not all cause economically relevant diseases. The grapevine leafroll, rugose wood complex, leaf degeneration and fleck diseases are the four main disorders having worldwide economic importance. In addition, new viral species and strains have been identified and associated with economically important constraints to grape production. In Brazilian vineyards, eighteen viruses, three viroids and two virus-like diseases had already their occurrence reported and were molecularly characterized. Here, we review the current knowledge of these viruses, report advances in their diagnosis and prospection of new species, and give indications about the management of the associated grapevine diseases.

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 score0.201
Threshold uncertainty score0.521

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 communication0.0010.001
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 head0.021
GPT teacher head0.313
Teacher spread0.293 · 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

Citations58
Published2017
Admission routes1
Has abstractyes

Explore more

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