Developing a gateway program for importing non‐animal origin ingredients from regions with African swine fever virus
Bibliographic record
Abstract
The US Department of Agriculture (USDA) categorizes the risk of African swine fever virus (ASFV) entry into the United States through non-animal origin feed ingredients as 'negligible to moderate, with high uncertainty'. Both Canada and Australia have implemented policies that are suggested to reduce the risk of ASFV entry through feed ingredients, but the United States has not because of scientific limitations that have been addressed by recent publications. As regulators and industry consider a potential pathway forward, the objective of this manuscript is to describe a process to determine if a voluntary or regulatory import policy is warranted by the United States. Initially, the volume and types of non-animal origin feed ingredients imported from countries with ASFV were quantified and assigned a level of risk (high risk: unprocessed grains and oilseeds, moderate risk: soybean co-products (meals, oil, and oilcake), and low risk: amino acids, vitamins, and other synthetically produced products from countries that have ASFV). In 2020, moderate- and high-risk ingredients from ASFV-positive countries represented 3.1% of all ingredients imported into the United States. Policies from Canada and Australia were evaluated for practicality of implementation by US government officials. Industry representatives from both countries consistently stated their policies would not be feasible in the United States due to the differences in cost and complexity of the swine and feed industries. Overall, unprocessed, or high-risk, ingredients from ASFV-positive countries represent a low percentage of imported ingredients into the United States; however, cautionary procedures may still be warranted given industry demand.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".