Assessment of Soy-Based Imports into the US and Associated Foreign Animal Disease Status
Bibliographic record
Abstract
Soy-based products are known to pose a viable risk to US swine herds because of their ability to harbor and transmit virus. This study evaluated soy imports into the US as a whole and from foreign animal disease positive (FAD+) countries to determine which products are being imported in the highest quantities and observe potential trends in imports from FAD+ 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 (US tons, T) and breakdown of different soy product types being imported into the US from 2015 to 2020. A total of 78 different countries exported soy products to the US in 2019 and 2020, with top contributors being Canada (602,377 T and 530,759 T, respectively), India (438,563 T and 474,678 T, respectively), and Argentina (134,610 T and 87,602 T, respectively). In 2020, soy oilcake (641,846 T) was imported in the largest quantities, followed by organic soybeans (297,838 T) and soy oil (148,190 T). 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 US from FAD+ countries in 2019 and 2020 were India (438,563 T and 474,678 T, respectively), Argentina (134,610 T in 2019), and Ukraine (44,415 T and 62,162 T, respectively). A system to monitor the sourcing of these products into the US and the end usage would allow for a greater understanding of the risk of these products to domestic 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".