Detection, Understanding and Controlof Soybean Mosaic Virus
Bibliographic record
Abstract
Among 67 or so viruses that are able to infect soybean, 27 are considered a threat to the soybean industry Soybean mosaic virus (SMV) is the most prevalent virus and is recognized as the most serious, long-standing problem in many soybean producing areas in the world SMV is a member of the genus Potyvirus in the family Potyviridae. The disease caused by SMV was first documented in the USA in 1915 by Since then, the virus has been found in China, Japan, South Korea, Canada, Bazil, Australia and many other countries wherever soybean is grown. Infection by SMV usually results in severe yield losses and seed quality reduction. It has been reported that yield losses usually range from 8 to 50% under natural field conditions Since SMV is a seed-borne viral pathogen and aphids can efficiently spread it from plant to plant while they feed, it is difficult to control the virus and produce SMV-free seeds. Furthermore, SMV often infects soybeans with other viruses such as Bean pod mottle virus (BPMV), Alfalfa mosaic virus (AMV) and Tobacco ringspot virus (TRSV) Such synergistic infections with two or more viruses cause much more severe damages than infection by each virus alone Utilization of soybean cultivars resistant to SMV is considered the most effective way of controlling the diseases. Extensive screening for soybean gemplasm resistant to SMV has resulted in the identification of three independent resistant genes, i.e.,
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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 | 0.001 | 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 | 0.000 | 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) | 0.001 | 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".