Impacts of avian influenza virus on animal production in developing countries.
Bibliographic record
Abstract
Abstract This paper reviews the (predominantly grey) literature on impacts of highly pathogenic avian influenza (HPAI) strain H5N1 and control responses on the livestock sector and associated industries in developing countries. The authors distinguish between impacts that arise directly through HPAI-related morbidity and mortality, those that are a consequence of public intervention to control or eradicate HPAI, and impacts that are mediated through market reactions. The paper further considers how these impacts propagate up- and downstream through related supply and distribution networks, how short-term reactions are followed by longer-term adjustments, how impacts include direct cost elements and foregone income, and why losses to the poultry sector will, at least to some extent, be 'passed on' on the one hand, for example through compensation, and, on the other hand, be compensated for by gains in other livestock subsectors. Differences in methodology applied in the reviewed reports result in a lack of comparability of estimates for HPAI 'costs/impacts' across countries and even within countries and are compounded by information deficits. Despite these shortcomings, the literature permits some significant conclusions to be drawn on the relative importance of direct and indirect impacts and on their distribution across different types of poultry producers. The paper ends by outlining directions of future research that combine epidemiology and economics to provide a framework for disease control decision-making.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".