Intervention to Improve Biosecurity System of Poultry Production Clusters (PPCs) in Thailand
Bibliographic record
Abstract
Widespread outbreaks of avian influenza occurred in 2004–2005. The outbreaks resulted in extensive losses for the poultry sector in East and South East Asia. Thailand suffered a tremendous impact from the disease. Later, in 2006, there was another outbreak of the aforementioned disease in poultry production clusters (PPCs) in Nakhon Phanom province in the northeastern region of Thailand. In this study, we conducted an intervention by working together with the Department of Livestock Development officials to improve the biosecurity level of PPCs in this province. The methods employed in the intervention included meetings to build understanding and hear about various ideas and problems among stakeholders; instructions; having the farmers perform self-evaluations of the level of biosecurity on the farms; and measures for motivating farmers, e.g., farm contests and handing out awards. The results revealed the following information: After intervention, attraction to wild bird of poultry farms in PPCs decreased (p < 0.05), because the farmers cut down trees around farm and poultry housing. Moreover, biosecurity system planning inside farms in PPCs increased (p < 0.05). The scores for biosecurity system planning inside farms that increased following the intervention are a positive sign that farmers will continue to develop better biosecurity systems on their farms.
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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 | 0.001 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".