Livestock and Risk Group 4 Pathogens: Researching Zoonotic Threats to Public Health and Agriculture in Maximum Containment
Bibliographic record
Abstract
Maximum-containment laboratories are a unique and essential component of the bioeconomy of the United States. These facilities play a critical role in the national infrastructure, supporting research on a select set of especially dangerous pathogens, as well as novel, emerging diseases. Understanding the ecology, biology, and pathology at the human-animal interface of zoonotic spillover events is fundamental to efficient control and elimination of disease. The use of animals as human surrogate models or as target-host models in research is an integral part of unraveling the interrelated components involved in these dynamic systems. These models can prove vitally important in determining both viral- and host-factors associated with virus transmission, providing invaluable information that can be developed into better risk mitigation strategies. In this article, we focus on the use of livestock in maximum-containment, biosafety level-4 agriculture (BSL-4Ag) research involving zoonotic, risk group 4 pathogens and we provide an overview of historical associated research and contributions. Livestock are most commonly used as target-host models in high-consequence, maximum-containment research and are routinely used to establish data to assist in risk assessments. This article highlights the importance of animal use, insights gained, and how this type of research is essential for protecting animal health, food security, and the agriculture economy, as well as human public health in the face of emerging zoonotic pathogens. The utilization of animal models in high-consequence pathogen research and continued expansion to include available species of agricultural importance is essential to deciphering the ecology of emerging and re-emerging infectious diseases, as well as for emergency response and mitigation preparedness.
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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 | 0.013 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".