Dynamics of Rice Brown Leaf Spot Disease (<em>Bipolaris oryzae</em>) Incidences Due to Seasonal Weather Differences in the Dry Zone of Sri Lanka
Bibliographic record
Abstract
Weather factors are key determinants in ecological disease management in sustainable agriculture, while judicious crop management systems deliver better control over rice diseases in tropical conditions. This study was designed to explore the effect of weather factors under different crop management systems and seasons on Rice Brown Leaf Spot (RBLS) disease incidences caused by Bipolaris oryzae in the tropical dry zone of Sri Lanka. The incidence of RBLS was measured under Conventional, Reduced, and Organic crop management systems commencing from the first occurrence of disease symptoms, at three-day sampling intervals in the tropical dry zone during wet (Maha) 2018/19 and 2019/20, and dry (Yala) 2019 and 2020 seasons. Secondary data on weather parameters were collected from the regional weather station. The RBLS incidences were highest in the wet season and were most abundant at the reproductive stage. The disease incidence dynamics over time were found to be similar among all the crop management systems in three seasons. The cumulative amount of rainfall seven days before the disease observation (RF7), the day-RH (DRH), and the maximum (TMAX48) and average temperature (TAVG48) that were recorded 48 h before the disease observations were found to be significantly correlated with the disease incidence of crop management systems in the wet season. DRH and minimum temperature (TMIN72) of 72 h before the disease observed in the wet season resulted in higher disease incidences. The RBLS disease can be managed concerning the crop management systems under high DRH and TMIN (20-25 ℃) in the wet season.
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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) | 0.000 | 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".