Molecular Characterization of Viruses Infecting Greenhouse Vegetables in Ontario
Bibliographic record
Abstract
Greenhouse vegetable production is a very important and successful agricultural sector in Ontario. However, newly emerging viral or virus-like diseases have become a major limiting factor in greenhouse vegetable production. To determine the identity of the viral pathogens in the three major greenhouse vegetables in Ontario, we conducted in-depth sequencing of small RNAs isolated from virally infected plants used next-generation sequencing technology. Subsequent bioinformatics analyses revealed six viral pathogens including Bell pepper endornavirus (BPEV), Parietaria mottle virus (PMoV), Cucumber green mottle mosaic virus (CGMMV), Tobacco streak virus (TSV), Pepper mild mottle virus (PMMoV), and Pepino mosaic virus (PepMV). The full-length genome sequences of BPEV, PMoV, and CGMMV have been cloned and sequenced.\nSince CGMMV causes the most severe economic lossess, we further characterized CGMMV and constructed an infectious CGMMV clone for the development of an attnuated virus for the control of CGMMV. We found that in the mimic greenhouse hydroponic production system, CGMMV is water-transmissible. We introduced various mutaitons into the CGMMV infectious clones and tested their infectivity. Our data demonstrated that when host plants were infected by two particular mutants MRTAL and MGVDT, no symptom appeared on the systemic leaves and viral RNA or coat protein accumulation level was low. Based on these data, we suggest that the mutants MRTAL and MGVDT have great potential to be used as attenuated CGMMV variants for cross protection against CGMMV.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 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".