ANIMAL HEALTH: THE POTENTIAL ROLE FOR LIVESTOCK DISEASE INSURANCE
Bibliographic record
Abstract
Animal diseases can cause significant production losses and a reduction in livestock receipts. While compensation is provided by the U.S. government in the event of an emergency disease outbreak, that compensation, an indemnity payment, does not cover the other costs that producers incur when their production cycle is interrupted. Those other losses, consequential costs, include business interruption, loss of markets, reduced productivity, increased welfare costs and increased biosecurity compliance costs. The recent Canadian experience with bovine spongiform encephalopathy (BSE, commonly referred to as mad cow disease) demonstrates the significance and magnitude of these other, market related, losses-- most significantly losses in exports. Federal and state governments have a role to play in minimizing disease risk because animal health has many of the characteristics of a public good. A healthy livestock herd not only provides adequate food but also ensures that zoonotic diseases1 are not transmitted to humans. Animal health is a public good managed by federal and state governments and by individual producers. Market incentives alone are insufficient to induce adequate supplies of animal health, so federal and state governments intervene to improve the supply of animal health. The actions taken by the U.S. to safeguard animal health are not readily understood or widely
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.007 | 0.003 |
| Insufficient payload (model declined to judge) | 0.042 | 0.002 |
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".