Feed safety collaborations: Experiences, progress and challenges
Bibliographic record
Abstract
Concerns were raised regarding the role feed and feed ingredients play for risk of disease introduction and dissemination after PEDV was first identified mid-2013. Subsequently there has been a body of research and reviews completed. The results suggest a subset of contaminated feed ingredients could serve as vehicles for transboundary disease introduction into the United States. That has led to the development of biosecurity information from the pork and feed industry associations. At this time, implementation is voluntary. In 2019, representatives from pork producers, veterinarians, pork and other agriculture commodity associations and animal food industry associations formed a feed safety task force. The United States Department of Agriculture, the United States Food and Drug Administration and the Canadian Food Inspection Agency were also invited and attended. The task force operates under the premise that all participants agree there is risk of introduction of pathogens into and within the US via imported feed products. It is agreed that any actions should be achievable, are based on science and should minimize trade disruptions. The pork and feed industries have the same goal - a healthy, productive US swine herd. While our two industry sectors may have different ideas on how to prevent the introduction of diseases via imported feed ingredients, there is agreement that the general foundation for these approaches must be science based, cost effective and minimize negative impacts on market and international trade. Noncompliance with voluntary mitigation measures puts the entire pork industry at risk, all allied industries, and the US agricultural economy in general. Because of that it is essential to continue to evaluate the role of effective regulation to ensure risk of introduction is minimized through implementation of programs that will be broadly and uniformly applied.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".