Assessment of Biotechnology Policies and International Trade in Key Markets for U.S. Agriculture
Bibliographic record
Abstract
The United States leads the world in agricultural biotechnology research, adoption, commercialization, and exports. Our biotech commodities are highly dependent on international markets. Thus, any biotech policy changes by key importing countries may affect U.S. agricultural biotech product exports. This article identifies key markets for U.S. agricultural exports including biotech commodities and discusses current and proposed biotech policies in key markets for U.S. agricultural exports focusing on Canada, Mexico, Japan, the European Union (EU), and China. Among these markets, labeling of biotech products is voluntary in Canada and Mexico but is mandatory in Japan, the EU, and, most recently, in China. For the EU, U.S. corn exports were almost completely shut out, while U.S. soybean exports also declined because of the EU's biotech policies. The World Trade Organization dispute filed by the United States has yet to be finalized. China's biotech regulations raised concern by U.S. agricultural exporters. However, through U.S. Department of Agriculture education programs, U.S.–China negotiations, and China's domestic soybean shortage, China's biotech regulations do not appear to have had long-run impacts on U.S. soybean exports to China.
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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 | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".