Characterizing the Virome of Apple Orchards Affected by Rapid Decline in the Okanagan and Similkameen Valleys of British Columbia (Canada)
Bibliographic record
Abstract
Rapid apple decline disease (RAD) has been affecting orchards in the USA and Canada. Although the primary cause for RAD remains unknown, viruses may contribute to the incidence or severity of the disease. We examined the diversity and prevalence of viruses in orchards affected by RAD in the Okanagan and Similkameen Valleys (British Columbia, Canada). Next-generation sequencing identified 20 previously described plant viruses and one viroid, as well as a new ilarvirus, which we named apple ilarvirus 2 (AIV2). AIV2 was related to subgroup 2 ilarviruses (42–71% nucleotide sequence identity). RT-PCR assays of 148 individual leaf samples revealed frequent mixed infections, with up to eight viruses or viroid detected in a single tree. AIV2 was the most prevalent, detected in 64% of the samples. Other prevalent viruses included three ubiquitous viruses from the family Betaflexiviridae and citrus concave gum-associated virus. Apple rubbery wood virus 1 and 2 and apple luteovirus 1 were also readily detected. The thirteen most prevalent viruses/viroid were detected not only in trees displaying typical RAD symptoms, but also in asymptomatic trees. When compared with reports from orchards affected by RAD in Pennsylvania, New York State, and Washington State, regional differences in relative virus prevalence were noted.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".