Influence of Cowpea Mottle Virus and Cucumber Mosaic Virus on the Growth and Yield of Six Lines of Soybean (Glycine Max L.)
Bibliographic record
Abstract
Cowpea mottle virus is of localised importance on cowpea in Nigeria infecting several leguminous species andCucumber mosaic virus is of worldwide distribution infecting several horticultural crops.A study was carried out in the screen house of the crop production pavilion, Faculty of Agriculture, University of Ilorin,Ilorin, Kwara State, Nigeria to determine the pathogenic response of six lines of soybean (TGx 1844-18E, TGx 1448-2E,TGx- 1910-8F, TGx 1019-2EN, TGx 1844-4E and TGx 1876-4E) to single and mixed infection with cowpea mottlevirus (CMeV) and cucumber mosaic virus (CMV).The Lines obtained from National Cereal Research Institute, Badeggiwere used for the experiment.Two viable Seeds were sown per 4litre plastic pots filled with sterilized soil; inoculation of viral isolates was carried outmechanically on the soybean at two weeks after planting.The results revealed that all Soybean lines tested were susceptible to single and mixed infection of the two viruses.However the tolerance level of the legumes, as shown by the percentage loss in selected growth and yield attributesindicated that line TGx 1910-8F is the most tolerant while TGx 1844-18E is the most susceptible to single and mixedinfection.Hence, TGx 1910-8F is most suitable in areas where there is an outbreak of disease caused by CMeV and CMV.
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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".