Animal Health: Foundation of a Safe, Secure, and Abundant Food Supply
Bibliographic record
Abstract
During the past century, reductions in animal diseases have resulted in a safer, more uniform, and more economical food supply. In the United States, the passage of the 1906 Federal Meat Inspection Act mandated better sanitary conditions for slaughter and processing, as well as inspection of live animals and their processed products. Following World War II, Congress passed the Poultry Products Inspection Act. Both acts are regulated by the Food Safety and Inspection Service (FSIS) of the US Department of Agriculture (USDA). The USDA's Animal and Plant Health Inspection Service (APHIS) is responsible for regulations governing the health of live animals prior to slaughter. This article is a brief overview of the ways in which the current predominance of zoonotics among emerging diseases underscores the importance of veterinary health professionals and the need for continued coordination between animal-health and public-health officials. Examples of intersections between animal- and public-health concerns include bovine spongiform encephalopathy (BSE) and Johne's disease, as well as extending beyond food safety to diseases such as avian influenza (AI). In the United States, we have in place an extensive public and private infrastructure to address animal-health issues, including the necessary expertise and resources to address animal-health emergencies. However, many challenges remain, including a critical shortage of food-animal veterinarians. These challenges can be met by recruiting and training a cadre of additional food-supply veterinarians, pursuing new technologies, collaborating with public-health officials to create solutions, and sending a clear and consistent message to the public about important animal-health issues.
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 | 0.011 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.008 | 0.010 |
| Insufficient payload (model declined to judge) | 0.026 | 0.010 |
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".