Distribution and Variability of Pseudocercospora griseola in Uganda
Bibliographic record
Abstract
Pseudocercospora griseola the casual pathogen for angular leaf spot (ALS) on common bean (Phaseolus vulgaris) is highly variable with many different races occurring in different agro-ecological zones. Therefore, for an effective breeding program, there is a need to continuously monitor the distribution and variability of the pathogen as a means of guiding breeders on which races to target. A study was conducted to assess the incidence, severity and variability of P. griseola in bean growing districts of Dokolo, Lira, Apac, Sironko, Mbale, Rakai, Lwengo, Bukomansibi, Kabale and Kisoro in Uganda. Forty five out of 80 isolates collected were tested on a set of 12 international bean differentials cultivars for ALS. Molecular markers were also used to elucidate the variability among isolates. In all districts, ALS was found wide spread with a mean incidence of 49.9%. Dokolo district had the highest disease incidence and severity, while Kisoro registered the lowest values respectively. In terms of altitude, incidence (60%) and severity (45%) were high for bean fields located between 1000-1200 m and low disease incidence (33.6%) and severity (20.7%) for fields lying above 1500 m. The standard differential cultivars and molecular markers defined 12 pathotypes and 30 haplotypes respectively. These belonged to Middle America and Andean group with considerably high level of diversity. Our findings indicated the presence of ALS in Uganda with high incidence, severity and variability across districts and altitudes. Hence strategic interventions involving integrated disease management and breeding for durable resistance are required to manage 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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".