Combatting <i>Sitophilus oryzae</i> in Rice: Strategies and Challenges
Bibliographic record
Abstract
Sitophilus oryzae , commonly known as the rice weevil, is a significant pest in rice production, causing considerable losses globally. Effective management of S. oryzae is crucial for ensuring food security and minimizing economic damage. This study provides a comprehensive overview of the biology and behavior of S. oryzae , including its life cycle, feeding habits, and the environmental factors that influence infestation levels. Current management strategies, including chemical, biological, physical, and cultural control methods, are critically evaluated, highlighting the challenges posed by resistance development and the limitations of biological controls. A detailed case study from a selected region illustrates the application and outcomes of integrated pest management (IPM) strategies, offering valuable lessons for broader application. This study underscores the need for innovative approaches, including advances in genetic research, novel biocontrol agents, and the integration of precision agriculture technologies, to enhance the effectiveness of S. oryzae management. Future research and policy recommendations are provided to support sustainable pest management practices and international collaboration in the ongoing battle against this persistent pest.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".