Distribution, Incidence and Severity of Maize Lethal Necrosis Disease in Major Maize Growing Agro-ecological Zones of Uganda
Bibliographic record
Abstract
The distribution, incidence and severity of maize lethal necrosis (MLN) disease in major maize growing agro-ecological zones (AEZ) of Uganda was determined following field surveys carried out in 16 major maize growing districts from 5 AEZ over three consecutive seasons. A total of 604 maize fields were visited and MLN disease status visually assessed and 3,624 maize leaf samples collected for identification and confirmation of the MLN causal viruses by Double antibody sandwich enzyme-linked immunosorbent assay (DAS-ELISA) and Reverse Transcription Polymerase Chain Reaction (RT-PCR). MLN disease was not widely distributed at an epidemic proportion, with only 36 (5%) of the 604 farms surveyed over three seasons confirmed to have the disease. The MLN incidence and severity was significantly (P < 0.05) higher in the Eastern AEZ during the three seasons. The main MLN-causing viruses detected using DAS-ELISA were Maize chlorotic mottle virus (MCMV) and Sugarcane mosaic virus (SCMV). MCMV was the most prevalent MLN causing virus driving the epidemic in Uganda. The three major districts where MLN disease has been found in all three seasons surveyed are Bulambuli, Tororo and Busia which are hotspots for MLN disease. Strategies to control spread of MLN disease should focus on high risk AEZs and hotspot districts.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".