Risk Management of Outbreaks of Livestock Diseases
Bibliographic record
Abstract
Livestock diseases can severely harm animal and human health, and have adverse economic impacts on producer incomes, markets, trade, and consumers. This paper develops a common framework to improve information on public actions and policies to manage outbreaks of livestock diseases across countries. The main aim is to facilitate the assessment of the effectiveness and efficiency of different policy responses to disease outbreaks. A pilot database covering four livestock diseases (avian influenza, bovine spongiform encephalopathy, classical swine fever, and foot and mouth disease) in nine countries (Canada, Denmark, France, Germany, Hungary, Japan, Mexico, the Netherlands, and the United Kingdom) was constructed. It combines three layers of data: epidemiological factors; government control and compensation measures; and economic impacts of disease outbreaks. Policy responses to outbreaks were reviewed based on the information generated from the data analysis. The results show that government expenditures to destroy pathogens via slaughter and compensation policy measures were very expensive, especially in the case of large or prolonged outbreaks, and that measures compensating financial losses at the farm level generated the highest share of government expenditures in the short run.
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 | 0.010 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".