Improving food safety in Asia through increased capacity in ecohealth
Bibliographic record
Abstract
Interest has increased considerably in the last five years in transdisciplinary approaches to addressing the precipitating factors of emerging infectious and zoonotic diseases. During this time, several One Health and ecohealth initiatives have begun in Asia. This paper reports on recommendations coming out of one such initiative (the Building Ecohealth Capacity in Asia project) and outlines a strategy for promoting an ecohealth approach in research and in practice relevant to prioritized concerns relating to reducing zoonotic disease in Asia. The three main aspects of the strategy that are presented and discussed include: (1) Promote transdisciplinary approaches to understanding the complexity of zoonotic disease that compromise food safety; (2) increase teaching and application of ecohealth in medical sciences and other subjects relevant to food safety; and (3) bring ecohealth and One Health approaches into health policy discussions, particularly where these discussions influence policy formulation. Main constraints to applying such a strategy include limited awareness and knowledge of ecohealth and One Health, lack of willingness to engage in a transdisciplinary setting, restricted capacity to change academic curricula, rigid institutional frameworks for problem solving, and availability of funding. Suggestions for reducing these constraints are addressed.816600
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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 | 0.011 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.003 | 0.017 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.024 | 0.003 |
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".