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Record W4416404165 · doi:10.1007/s10327-025-01264-x

Resistance and tolerance to viroid infection: status and prospects

2025· article· en· W4416404165 on OpenAlexaff
Takashi Naoi, Zhixiang Zhang, Charith Raj Adkar‐Purushothama, Jean‐Pierre Perreault, Teruo Sano

Bibliographic record

VenueJournal of General Plant Pathology · 2025
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant Virus Research Studies
Canadian institutionsUniversité de Sherbrooke
Fundersnot available
KeywordsViroidContext (archaeology)Plant virusGenomeGene silencingPlant disease resistancePotato spindle tuber viroidGene

Abstract

fetched live from OpenAlex

Abstract Viroids are the smallest known plant pathogens. Although their genome sequences are not known to encode any peptides, they can nonetheless replicate and cause disease in several plant species. One of the goals of research into viroids is to safeguard crops from viroid diseases and hence limit the economic losses to acceptable levels. In order to control viroid disease epidemics, and to mitigate the damage they cause, various different approaches have been used. These approaches include the development of viroid-resilient plants through breeding programs and the grafting of high-yield varieties onto viroid-resistant rootstocks. Furthermore, biotechnological approaches—such as the transgenic expression of ribonuclease, ribozymes, antisense RNA, and hairpin RNA—have been tested against various viroid-host combinations. In this review, we summarize the current understanding of both the natural genetic resources available with either viroid resistance or disease tolerance and the gene-editing technologies that is available for enhancing viroid resistance/tolerance. Additionally, we discuss currently available technologies with which plant viral diseases can be managed (e.g., spray-induced gene silencing and genome editing) in the context of their application to future viroid research.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.939
Threshold uncertainty score0.126

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 head0.017
GPT teacher head0.258
Teacher spread0.242 · 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 designBench or experimental
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

Citations0
Published2025
Admission routes1
Has abstractyes

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