Epidemiological Assessment of Cassava Mosaic Disease in a Savanna Region of the Democratic Republic of Congo
Bibliographic record
Abstract
An epidemiological survey was conducted, from September 2009 to January 2010 in 206 famers’ fields located across 21 cassava-growing localities of Ngandajika territory in Lomami province (central part of Democratic Republic of Congo (DRC), to determine distribution and status of Cassava Mosaic Disease (CMD). Parameters related to the identification and the evaluation of CMD (incidence, severity and gravity) and number of adult whitefly vector were assessed. CMD was present in all localities surveyed and varied according to the localities and varieties. CMD incidence was low at the INERA station (4.33%) but high in Kafumbu (74.55%). Disease severity was low at the INERA station (1.09) but high in Kafumbu (2.67). Gravity was low at the INERA station (2.99%) and elevated in Quartier Congo (67.82%). The mean of adult whitefly populations varied with sites. However, the whiteflies were more abundant in Mpunga (3.65) compared to the INERA station (1.09). Overall, 71 % of varieties showed varying degrees of sensitivity to CMD. The results of this study revealed that the health status of cassava in Ngandajika is alarming and deserves sustained intervention. Adequate and effective control methods must to be in place to reduce the inoculum levels and the speed of CMD propagation and to limit yield losses caused by this disease.
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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) | 0.000 | 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".