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Record W1589288743 · doi:10.1017/s1074070800006854

Assessment of Biotechnology Policies and International Trade in Key Markets for U.S. Agriculture

2005· article· en· W1589288743 on OpenAlexaboutno aff
Mary A. Marchant, Baohui Song

Bibliographic record

VenueJournal of Agricultural and Applied Economics · 2005
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicGenetically Modified Organisms Research
Canadian institutionsnot available
Fundersnot available
KeywordsChinaAgricultural biotechnologyInternational tradeAgricultureCommercializationBusinessEuropean unionEconomic shortageNegotiationProduct (mathematics)BiotechnologyAgricultural economicsEconomicsPolitical scienceMarketingBiologyLaw

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.968
Threshold uncertainty score0.148

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.240
Teacher spread0.224 · 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 teacher head, 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

Citations8
Published2005
Admission routes1
Has abstractyes

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