Assessment of soy‐based imports into the United States and associated foreign animal disease status
Bibliographic record
Abstract
Soy-based products are known to pose a viable risk to U.S. swine herds because of their ability to harbour and transmit virus. This publication aimed to evaluate soy imports into the United States as a whole and from foreign animal disease positive (FAD-positive) countries to determine which products are being imported in the highest quantities and observe potential trends in imports from FAD-positive countries. Import data were accessed through the United States International Trade Commission website (USITC DataWeb) and summarized using R (version 4.0.2, R core team, Vienna, Austria). Twenty-one different Harmonized Tariff Schedule (HTS) codes were queried to determine quantities (metric tonnes, MT) and breakdown of different soy product types being imported into the United States from 2015 to 2020. A total of 78 different countries exported soy products to the United States in 2019 and 2020 with top contributors being Canada (546,467 and 481,497 MT, respectively), India (397,858 and 430,621 MT, respectively) and Argentina (122,116 and 79,471 MT, respectively). Soy oilcake (582,273 MT) was imported in the largest quantities, followed by organic soybeans (270,194 MT) and soy oil (134,436 MT) for 2020. Of the 78 countries, 46 had cases of FAD reported through the World Organization for Animal Health (OIE) World Animal Health Information Database (WAHIS). Top exporters of soy products to the United States from FAD-positive countries in 2019 and 2020 were India (397,858 and 430,621 MT, respectively), Argentina (122,116 MT in 2019) and Ukraine (40,293 and 56,392 MT, respectively). The risk of FAD introduction to the United States through soy imports can fluctuate based on where FAD outbreaks are occurring, shipping methods and end usage of products. A system to monitor these factors could help make future decisions about trade and risk of FAD introduction to U.S. swine herds.
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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 | 0.000 | 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".