Quantification of soy-based feed ingredient entry to the United States by ocean freight shipping and the associated seaports
Bibliographic record
Abstract
The potential of feed ingredients to serve as vehicles for African Swine Fever Virus (ASFV) introduction to the US is a significant concern. ASFV DNA has been detected in the Chinese feed system; raw grains and meals drying on the ground and milling facilities and feed delivery vehicles. Experimental evidence of ASFV survival in multiple soy-based feed ingredients during a simulated 30-day transoceanic journey and the transmission of ASFV through the natural consumption of contaminated feed has been published. Therefore, it’s important to understand the quantity of soy-based ingredients that enter the US from ASFV-positive countries via ocean shipping and rank sea ports of Entry (POEs) according to annual volume of these products to manage this risk. The quantity of soy-based feed ingredients and their specific ports of entry was obtained at the International Trade Commission Harmonized Tariff Schedule website ( www.hs.usitc.gov ), a publically available website that provides a transaction of specific trade commodities between the US and its international trading partners. A close review of this database identified 10 HTS codes pertaining to soy-based feed ingredients, including soybeans, soybean meal, soy oil cake and soy oil. Specific queries on these 10 HTS codes were designed to provide information on country of origin, quantity of product, date of entry, and POE into the US. Data were exported into Microsoft Excel, then organized into pivot tables that described the quantity of specific product by country of origin and POE. The analysis focused on the 43 ASFV-positive countries on the Canadian Food Inspection Agency Watch List. In 2018, 104,707 metric tons (MT) of soy-based ingredients were imported to the US from a total of nine foreign countries that are included on the CFIA Watch List. 52.6 % of this volume that was imported, or 55,101 MT, originated from China. These soy-based products from China entered the US from a total of 13 separate ports of entry (POEs). Of these POEs, a total of 4 POEs received greater than 88% of all of soy-based ingredients originating from CHina, including San Francisco/Oakland, CA (60.36%), Seattle, WA (20.54%), Baltimore, MD (4.13%), and Los Angeles, CA (3.78%). This is a new approach to analyze the risk management of feed imports, focusing on seaport of highest risk and quantity of product received. This work represents an initial step towards building a comprehensive listing of imported products introduced into the pork supply chain, and provide a roadmap to understanding risks involved in global livestock feed ingredient sourcing.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".