Reaction of Selected Citrus Cultivars to Pseudocercospora Leaf and Fruit Spot Disease Under Natural Infection in Northern Uganda
Bibliographic record
Abstract
Citrus is an important crop among many resource constrained subsistence farmers living in rural areas of Uganda. Citrus production is affected mainly by drought, declining soil fertility, pests and diseases. Among diseases, citrus leaf and fruit spot disease caused by fungus Pseudocercospora angolensis is currently one of the major constraints to the production of citrus in Uganda where millions of people rely on the crop for nutritional security and household income. The disease can lead to 50-100% fruit yield loss depending on environmental conditions, disease management and weather conditions. In order to identify resistance to P. angolensis, a study was conducted to identify variability for citrus leaf and fruit spot resistance from adapted commercial cultivars as an initial step in developing integrated disease management strategy. Six cultivars were assessed. The screening was under natural infection conditions in disease hot spots in northern Uganda in 2014a and 2014b seasons. The results showed significance difference (p < 0.01) for Area Under Disease Progressive Curve (AUDPC) for number of leaves with P. angolesnis symptoms and number of lesions. Subsequently, the study identified Kuno as resistant and Tangelo as less susceptible to Pseudocercospora leaf and fruit spot infection, and they could be recommended for citrus leaf and fruit spot disease control.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".