Sequencing and Analysis of the Complete Genome of the Russian Cherry Virus A Isolate
Bibliographic record
Abstract
Abstract Virus diseases of stone fruit crops (Prunus spp.) reduce both fruit yield and quality and shorten the productive life of fruit trees. Cherry virus A (CVA, genus Capillovirus, family Betaflexiviridae) is one of the most common among more than 50 known viruses affecting these crops. A sweet cherry (P. avium) tree of the cultivar Iput with virus-like rugosity symptoms on the leaves was found during a survey of stone fruit collections of the Tsitsin Main Botanical Garden of the Russian Academy of Sciences (Moscow). When studying the virome of this plant using high-throughput sequencing, reads related to CVA were generated, and the complete genome of a new isolate of this virus, named SwC14, was assembled. Typical of capilloviruses, the SwC14 genome contained two open reading frames encoding a viral replicase, a coat protein, and a movement protein. The SwC14 genome sequence was shown to be closest to the genomes of some Canadian sweet cherry CVA isolates (99.4% identity) and 81.4–95.0% identical to isolates from other stone fruit species and other regions of the world, including previously characterized Crimean isolate PTC (82.0% identity). CVA was also detected in six of the seven other symptomless cherry cultivars examined by RT-PCR. This is the first report of a divergent CVA isolate in Central Russia, which expands information on the geographical distribution and genetic diversity of the virus.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".