Effect of soybean vein necrosis on yield and seed quality of soybean
Bibliographic record
Abstract
Soybean vein necrosis virus (SVNV) rapidly became a widespread virus of soybean (Glycine max (L.) Merr.) in the USA and Canada within a few years of its initial detection in 2008; however, the economic impact of soybean vein necrosis (SVN) symptoms caused by virus infection remains unknown. Field studies were conducted in six states in the USA during 2013, 2014 and 2015 to determine the effect of SVN on soybean yield and seed quality. Quantitative parameters, including seeds per pod, pods per plant, yield and 100-count seed weight, were assessed from plants or seeds collected from research and commercial production fields. Qualitative parameters, including protein and oil concentration, were also obtained from samples collected in Indiana and Iowa. Results from all states suggest that yield is not impacted by SVN; however, seed quality was affected in four of seven location-years. In Iowa, oil concentration decreased by 0.11% as disease incidence increased by 1% (P = 0.04). In Indiana, SVNV infected plants exhibited decreased total oil content compared with asymptomatic plants (0.16% (P = 0.04); 0.67% (P > 0.01) in 2014 and 2015, respectively. These results suggest that SVN may change soybean seed quality, which may affect the marketability of soybeans for premium markets, specifically those interested in high oleic soybeans.
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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 | 0.000 | 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".