Facing <i>Rose rosette virus</i> : A risk to European rose cultivation
Bibliographic record
Abstract
Abstract Roses ( Rosa ) are one of the most valuable ornamental flowering shrubs around the globe. They are susceptible to numerous pathogens that require management, increasing the cost of cultivation. Rose rosette virus (RRV; genus Emaravirus ) is a devastating virus that has been spreading since the 1940s in the United States and Canada. It is an emerging risk to European and worldwide rose cultivation, causing symptoms such as witches' broom, malformations, excessive thorn production, and eventually plant death. RRV is transmitted by the eriophyid mite Phyllocoptes fructiphilus and by grafting. Research is being undertaken to understand RRV and to find control measures and resistant cultivars, as they are not currently available. Early detection of the disease is the key to prevent the establishment and spread of RRV and its vector. Different molecular and serological diagnostic methods have been designed and implemented, including ELISA, RT‐PCR, RT‐qPCR, LAMP, and high‐throughput sequencing. RRV infected plants can remain symptomless for long periods, so these diagnostic assays are necessary in conjunction with visual assessment to facilitate early detection. Significant social, economic, and environmental impacts are expected if RRV and its vector establish and spread in Europe. Rose trade between countries is the most likely pathway of introduction of RRV into Europe. In this review we describe current knowledge about RRV, the molecular and serological methods available for the detection of this virus, pathways to entry, and the possible impact if it establishes and spreads in Europe.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".