Rice Blast Prevalence in Smallholder Rice Farmlands in Uganda
Bibliographic record
Abstract
Rice blast disease remains the most important contributor to low and stagnated rice yields in Uganda. However, the role of the smallholder farming system in shaping the prevalence of the disease in the country is not known. In 2015B and 2016A, we surveyed smallholder rice farmlands in 27 districts of Uganda and recorded blast incidence, severity, and symptoms expression. Infected rice samples taken from the infected plants were sub-cultured on PDA media to confirm the pathogen and obtain isolates for the establishment of a core collection for breeding work. Rice blast prevalence in the districts varied from 50-100% and the national average stood at 72.61%, higher than that recorded five years ago. Mean incidence and severity varied significantly (< 0.001) with the highest incidence (96.8%) recorded in Luwero district and the least (21.3%) was recorded in the Amuru district. However, the eastern region recorded the highest average incidence (74.5%) followed by the central, the northern, and Mid-western regions. In the rice ecologies, the highest blast incidence was recorded in the rain-fed lowland rice (72.18%) followed by irrigated lowland (59.53%) and rain-fed upland rice (47.27%). This is the first report on the prevalence of blast in smallholder rice farmlands in Uganda and showed a higher prevalence of the 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 | 0.001 | 0.001 |
| 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".