The Foot-and-Mouth Disease Epidemic of 2001 in the UK: Implications for the USA and the “War on Terror”
Bibliographic record
Abstract
While there is no evidence to suggest that the recent epidemic of foot-and-mouth disease (Fmd) in the Uk and its subsequent spread to continental Europe were caused by bioterrorism, the extent of the epidemic shows that Fmd could be a very powerful weapon for a bioterrorist wishing to cause widespread disease in livestock and economic disruption for the targeted country. This paper describes the epidemic. It then examines the contentious issues that arose through the use of extensive slaughter to control the epidemic and explores how, in turn, the concerns of society are being translated into a radical change in policy within the European Union with respect to the control of Fmd and other foreign animal diseases. The crisis generated by the Fmd epidemic in Europe in 2001 provides many lessons to be learned for the US and highlights the need for creative thinking in research and teaching within colleges of veterinary medicine to more effectively address the threat of epidemic diseases under the "new world order." There is general agreement that the veterinary profession in the US plays a unique role in protecting the nation against epidemic livestock diseases, whether caused naturally or through bioterrorism. The profession also has a significant role in protecting the public's health, since several epidemic diseases of animals, such as rift valley fever, are zoonotic. However, improved financial support at the federal and state levels is urgently needed to support epidemic-diseases research and teaching in colleges of veterinary medicine.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.005 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".