Incidence, Severity and Gravity of Cassava Mosaic Disease in Savannah Agro-Ecological Region of DR-Congo: Analysis of Agro-Environmental Factors
Bibliographic record
Abstract
African Cassava mosaic disease (ACMD) is the most severe and widespread disease caused by viruses limiting production of the crop in sub-Saharan Africa. The objective of the present study was to evaluate CMD incidence, severity, and gravity under different agro-environmental conditions. A total of 222 fields were surveyed in 23 different locations. All the farmers grow only local cassava varieties without applications of fertilizers. Overall, mean CMD incidences for all sites surveyed were 58.2% and 51.7%, in 2009 and 2010, respectively. Disease severity ranged from 2.4 to 3.1 on a scale of 1 to 5. Mean disease gravity varied from 29.7% to 62%, in 2010, and 2009, respectively. Detailed analysis of agronomic and environmental revealed no significant association between cassava stand locations, age, land topography and the development of CMD. Likewise intercropping practices and field topping did not affect the development of CMD in all the fields surveyed. There were significant differences in the number of white flies (Bemisia tabaci) per plant in 2009 and 2010, but no significant correlations between the number of B. tabaci per plant and CMD incidence, severity, and gravity was found. In most fields, CMD appears to originate mostly from unhealthy cassava cuttings used for planting.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".