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Record W7056599029

The Future of U.S. Agricultural Biotechnology and Trade: Summary of a Roundtable Discussion

2023· article· en· W7056599029 on OpenAlexaboutno aff

Bibliographic record

VenueLincoln (University of Nebraska) · 2023
Typearticle
Languageen
FieldPhysics and Astronomy
TopicMagnetic confinement fusion research
Canadian institutionsnot available
Fundersnot available
KeywordsAgricultureFood securityGovernment (linguistics)Agricultural biotechnologyPopulationPaceSustainabilityWorld population
DOInot available

Abstract

fetched live from OpenAlex

The United States is one of the largest and highest-yield agricultural producers and exporters in the world. But the world’s population is growing, and growing conditions themselves are changing. Farms face shifting weather patterns, more frequent and severe storms, floods, and drought, and changing pest and disease pressures. What farmers plant and how they plant it are evolving to keep pace with these changes. Future global food security, sustainability, and U.S. leadership in agriculture will depend on the domestic and international policy and regulatory landscapes for agricultural biotechnology innovation. The Clayton Yeutter Institute of International Trade and Finance at the University of Nebraska-Lincoln convened a roundtable on March 2, 2023, conducted under the Chatham House Rule, to discuss the pressing issues around the future of U.S. agricultural biotechnology and trade. This was a unique opportunity to bring together expertise from the farm, the field, the lab, industry, and the policy arena. Participants included high-level government officials from the current and previous administrations, farmers, academics, and practitioners in plant genetics and agricultural sciences. This report summarizes that discussion. Points of widespread agreement among participants include: 1. Sustainability and food security require innovation. The world’s population continues to grow, growing conditions are changing, and our land and water resources are finite. We need to increase yield and there is no way to do this without innovation. 2. There are three agencies responsible for regulating genetically engineered (GE) crops in the United States—the U.S. Food and Drug Administration (FDA), U.S. Department of Agriculture (USDA), and the U.S. Environmental Protection Agency (EPA). (GE here includes both genetically modified and gene edited. We will use the broader term GE in this report.) Each agency has developed its own specific rules and regulations within the confines of the statutes, but different statutes govern each agency, which has resulted in “stovepipes that are crooked and turned in different buildings.” The U.S. regulatory regime for agricultural biotechnology needs to be streamlined, otherwise investors could go elsewhere like Brazil. 3. There is a fundamental misunderstanding in the United States about how our food is grown. Consumer labeling is helpful, but there is frustration that consumers are being oversold on the value of genetically modified organism (GMO)-free labels. The need for clear, science-based communication applies across the entire supply chain—farmers, processers, packagers, marketers, and grocers. 4. Transparency and science-based regulatory approaches by our trading partners are often as important as traditional market access. The World Trade Organization (WTO) Sanitary and Phytosanitary (SPS) Agreement has withstood the test of time, and the U.S. should work to defend it. SPS provisions are important because they incorporate the science and risk analysis provisions. The U.S.-Mexico-Canada Agreement (USMCA) was the first U.S. trade agreement to include agricultural biotechnology provisions, and that agreement and even the U.S.-China Phase One agreement are good examples of the way forward, although even stronger and bolder provisions could be sought in future agreements. 5. The importance of agricultural biotechnology innovation is not lost on China, and government leaders have a top-down plan to dominate in agricultural seeds and innovation. “Seeds are the ‘chip’ of agriculture,” said Han Wenxiu, deputy director of the Office of the Central Economics and Financial Affairs Commission, referring to semiconductors and the U.S.-China technology race.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.018
metaresearch head score (Gemma)0.013
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.031
Threshold uncertainty score0.094

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0180.013
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0020.002
Science and technology studies0.0080.002
Scholarly communication0.0180.011
Open science0.0040.006
Research integrity0.0310.018
Insufficient payload (model declined to judge)0.0260.007

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.

Opus teacher head0.007
GPT teacher head0.207
Teacher spread0.200 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreOther

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".

Quick stats

Citations0
Published2023
Admission routes1
Has abstractyes

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