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Record W2025497698 · doi:10.1080/07060660309507060

Multiple virus and viroid detection and strain separation via multiplex reverse transcription – polymerase chain reaction

2003· article· en· W2025497698 on OpenAlexaffvenue
R. P. Singh, Xianzhou Nie

Bibliographic record

VenueCanadian Journal of Plant Pathology · 2003
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant Virus Research Studies
Canadian institutionsAgriculture and Agri-Food Canada
Fundersnot available
KeywordsMultiplexReverse transcription polymerase chain reactionVirologyReverse transcriptasePolymerase chain reactionMultiplex polymerase chain reactionViroidMolecular biologyStrain (injury)VirusBiologyGeneGeneticsPlant virusMessenger RNA

Abstract

fetched live from OpenAlex

Vegetatively propagated plants free from virus- or viroid-induced diseases are integral of the success of many agricultural and horticultural sectors. Reverse transcription – polymerase chain reaction (RT–PCR) has emerged as an effective method to detect the presence of causal agents and has been used as a management strategy to ensure virus- or viroid-free propagation. Advances in understanding virus and viroid genome structures and the availability of genomic sequences of viruses and viroids (and their strains) have made it possible to create primers for specific RT– PCR. This has permitted RT–PCR to evolve into a technique that can simultaneously detect multiple pathogens in a single sample. Multiplex RT–PCR can reduce cost and save time. A further modification of multipex RT–PCR, termed competitive RT–PCR, with a single-antisense and multiple-sense primers, has been developed. It facilitates simultaneous identification and differentiation of a group of virus strains. The methodology of multiplex RT–PCR and its modified (competitive) RT–PCR version for potato virus and viroid detection and strain differentiation are reviewed here.

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 score0.715
Threshold uncertainty score0.670

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 head0.026
GPT teacher head0.228
Teacher spread0.202 · 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

Citations33
Published2003
Admission routes2
Has abstractyes

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