Influence of Seedling Age at Inoculation and Cultivar on the Pathogenicity of a Virus Causing Yellow Mosaic Disease of Commelina Benghalensis L. on Cowpea
Bibliographic record
Abstract
A screenhouse experiment was conducted to evaluate the influence of seedling age at inoculation and cultivar onpathogenicity of the virus causing yellow mosaic disease of Commelina benghalensis L, a broad leaf weed, on cowpea.Three cowpea varieties namely Vita 5, IT84S2246D and Ife Brown were grown in pots and inoculated with sapextracted from leaves of C. benghalensis infected with yellow mosaic disease at 7, 14, and 21 days after germination(DAG). It was found that inoculation of cowpea seedlings at 7 DAG subsequently led to the most severe symptoms,which were manifested by mosaic and yellowing of leaves and eventual poor growth and yield attributes. On the otherhand, plant growth and yield attributes that were comparable to those of the healthy control plants were recorded forplants inoculated at 21 DAG. Specifically, in regards to the interaction effects, cv. Vita 5 that were sap-inoculated at 7DAG had the lowest yield attributes, while cv. IT84S2246D inoculated at 21 DAG had the highest yield attributes. Theresults put together showed that although the yellow mosaic virus of C. benghalensis was sap-transmissible andpathogenic to cowpea causing characteristic yellow mosaic disease symptoms and reduction in yield attributes, severityof the disease is less if infection occurs at older stage of cowpea growth.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".