Using Expert Knowledge to Understand Biosecurity Adoption Aimed at Reducing Tier 1 Disease Risks in the U.S. Livestock Industry
Bibliographic record
Abstract
Using primary data from a survey of swine, beef cattle, and dairy industry experts in the United States, this study provides insights into adoption of biosecurity measures aimed at reducing Tier 1 disease risks. Experts believe the swine industry would see the highest and the beef cattle industry would see the lowest biosecurity adoption in the first year of a large Tier 1 disease outbreak. Risk reduction has a positive marginal effect on biosecurity adoption, and a firm’s own risk reduction matters as well as their closest neighbor’s risk reduction. Costs have a negative marginal effect on biosecurity adoption. A key reason explaining partial adoption might be that experts believe industry-wide biosecurity investment would likely bring benefits primarily to downstream sectors in the supply chain and producers would bare most of the costs. More educational materials available to explain Tier 1 disease risks and the benefits of risk mitigating biosecurity measures is found to be the least important factor for adoption and implementation of new, additional biosecurity measures. A producer or neighbor having personally experienced a Tier 1 disease on their operation, a producer’s view on their own likelihood of experiencing a Tier 1 disease given their current situation, and a producer’s view on effectiveness in reducing Tier 1 disease risks are found to be the most important factors. Understanding how several factors might impact biosecurity adoption aimed at reducing Tier 1 disease risks is necessary for the development of practices and policies that could reduce the impact of such disease incursions.
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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 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".