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Record W2167778600

Issues on Adoption, Import Regulations, and Policies for Biotech Commodities in China with a Focus on Soybeans

2003· article· en· W2167778600 on OpenAlexaboutno aff
Mary A. Marchant, Cheng Fang, Baohui Song

Bibliographic record

VenueMOspace Institutional Repository (University of Missouri) · 2003
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicGenetically Modified Organisms Research
Canadian institutionsnot available
Fundersnot available
KeywordsChinaAgricultureAgricultural biotechnologyEuropean unionCommodityBiotechnologyBusinessRapeseedAgricultural scienceAgricultural economicsInternational tradePolitical scienceEconomicsBiologyAgronomyLaw
DOInot available

Abstract

fetched live from OpenAlex

Since the introduction of biotech commodities in 1996, farmers in the United States have rapidly adopted this new technology for production, primarily for soybeans, cotton, and corn (Nelson, 2001). The United States is the largest grower of biotech crops in the world, with 101.5 million acres under cultivation in 2003 (United States Department of Agriculture [USDA] National Agricultural Statistics Service [NASS], 2003). In the United States, adoption of biotech soybeans reached 81% in 2003, 73% for biotech cotton, and 40% for biotech corn. Globally, in 2002 about 45% of soybean acreage was planted with biotech soybeans, 11% with biotech corn, 20% with biotech cotton, and 11% with biotech rapeseed (USDA Foreign Agricultural Service [FAS], 2003a). China has become the fourth largest grower of transgenic commodities, following the United States, Argentina, and Canada. China’s dominant biotech commodity is Bt cotton, with 5.2 million acres planted in 2002 (USDA FAS, 2003a). As of this writing, China has not approved the adoption of other major transgenic agricultural commodities, such as soybeans, corn, rice, or wheat. Given food safety concerns, there is global controversy about biotech foods. Many countries (particularly developed countries that import food) have implemented regulations to restrict adoption and import of biotech food products. In the late 1990s, six European Union (EU) member nations (Austria, France, Germany, Greece, Italy, and Luxembourg) banned imports of transgenic corn and rapeseed that were approved by the European Union (USDA FAS, 2003b). In late 1998, the EU imposed a five-year de facto moratorium on approving new transgenic varieties, which effectively prohibits most US corn exports to Europe. In May 2003, the United States, Argentina, and Canada filed a World Trade Organization (WTO) dispute against the EU over its moratorium (USDA, 2003; USDA FAS, 2003b). “The first step in a WTO dispute is to request and conduct consultations during the next 60 days. WTO procedures were designed to encourage parties to resolve their differences” (USDA FAS, 2003b). However, after consultations, in August 2003 the US took the next step by requesting a dispute settlement panel to hear arguments in its WTO challenge to the EU’s biotech moratorium. “Dispute settlement procedures, including appeal, typically take a total of 18 months” (USDA FAS, 2003b, 2003f). Japan also has strict regulations for biotech food imports. In 2000, Japanese legislation was introduced to prevent imports of food products that contain transgenic varieties not yet approved in Japan (USDA FAS, 2003d). Japan’s biotech testing focuses on transgenic products approved for commercialization abroad but not yet approved in Japan (e.g., StarLink corn is not approved for any use in Japan). In Japan, foods found Mary A. Marchant University of Kentucky

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)0.008
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.156
Threshold uncertainty score0.310

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.003
Science and technology studies0.0030.002
Scholarly communication0.0030.002
Open science0.0010.002
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.016
GPT teacher head0.208
Teacher spread0.193 · 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 designObservational
Domainnot available
GenreEmpirical

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

Citations21
Published2003
Admission routes1
Has abstractyes

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