Effect of Okra Yellow Vein Mosaic Virus (OYVMV) on Plant Growth and Yield
Bibliographic record
Abstract
Okra yellow vein mosaic virus (OYVMV) is one of the most destructive diseases of okra plant. In the current study, effect of okra yellow vein mosaic virus (OYVMV) was assessed on plant growth and yield in naturally infected crop under agro-ecological conditions of Hyderabad district. The virus showed the significant reduction in plant height, number of leaves, flowers, fruits, and over all pickings and yield of all the locations wherever the crop was examined in the district. The significant reduction in plant height (48.67 cm) in infected plants as compared to healthy plants (62.96 cm) was recorded. Similarly, significant difference in the flowers formation per plant at all four locations was recorded in diseased (0.912) and healthy (2.165) plants. Fruit weight was also significantly reducing due to the disease prevalence at all four locations (73.25 g) as compared to healthy observed fruits (91.50 g). Interestingly, on overall basis there were more numbers of leaves (20.66) in infected plants as compared to healthy one (16.33). It is obvious from the results that virus (OYVMV) showed significant increase in number of leaves but reduced plant height, flowers, fruits and yield at all four observed locations, thus, pathologists and breeders are advised to work more on evaluation of resistant varieties using advanced molecular tools. The growers are also advised to adopt preventive as well as curative control measures so that the yield losses may be decreased.
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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 | 0.001 |
| 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".