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Record W4391452598 · doi:10.1111/jph.13270

Molecular characterization of mungbean yellow mosaic India virus infecting <i>Vigna radiata</i> in Oman

2024· article· en· W4391452598 on OpenAlexfundno aff
Muhammad Shafiq Shahid, Abdullah M. Al‐Sadi

Bibliographic record

VenueJournal of Phytopathology · 2024
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant Virus Research Studies
Canadian institutionsnot available
FundersInstitute of Genetics
KeywordsVignaBiologyRadiataMosaic virusMosaicBotanyVirologyPlant virusVirus

Abstract

fetched live from OpenAlex

Abstract The full‐genome sequences of a bipartite begomovirus were identified in a mungbean (Vigna radiata) crop displaying symptoms such as yellow mosaics and stunting in the Al‐Batinah North region (23.6867° N 57.9058° E), Oman. Initial detection and confirmation of the virus genome were carried out through polymerase chain reaction, followed by obtaining complete genomes using rolling circle amplification. Bioinformatics analysis on both genomic components, DNA‐A and DNA‐B, revealed over 99% nucleotide sequence identity to the mungbean yellow mosaic India virus (MYMIV), previously known to infect tomato plants. Pairwise sequence analysis using the STD tool and subsequent phylogenetic analysis unveiled that the DNA‐A of the MYMIV isolate formed a cluster with closely related DNA‐A sequences of MYMIV from GenBank, while the DNA‐B clustered with the corresponding DNA‐B of the MYMIV isolate. Additionally, no evidence of recombination events was observed in the recombination analysis. Similarly, the nucleotide substitution rates between the DNA‐A and DNA‐B segments of MYMIV did not show significant differences. This finding represents the first documented instance of bipartite MYMIV infecting V. radiata in Oman.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.006
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
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 head0.013
GPT teacher head0.249
Teacher spread0.236 · 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 source (direct Gemma or distilled Codex), 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
Published2024
Admission routes1
Has abstractyes

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