Gap Assessment of Animal Health Legislation in Sri Lanka for Emerging Infectious Disease Preparedness
Bibliographic record
Abstract
Legal preparedness is critical for emerging infectious disease (EID) management. The authors develop a framework for assessing Sri Lanka's animal health legislation in order to support EID preparedness. The most comprehensive set of policies addresses highly pathogenic avian influenza. Key deficiencies included (a) the lack of a legislative framework for veterinary public health that could support the necessary institutional structure and responsibilities, (b) the lack of requirements to report a broad set of zoonotic diseases, (c) the lack of authority for animal health agencies to control zoonotic food-borne diseases, and (d) the lack of authority to impose and enforce animal health standards. Such policy deficiencies partially reflect the government's focus on livestock production for national self-reliance, rural development and nutrition enhancement rather than for international trade. The steps now being taken to remedy these problems concentrate on creating an enhanced capacity for the early detection of disease. This study highlights the need to develop evidence-based criteria for EID policy.
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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 | 0.022 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.010 | 0.007 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".