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Record W2074968226 · doi:10.4161/21645698.2014.945880

Risk, regulation and biotechnology: The case of GM crops

2014· article· en· W2074968226 on OpenAlexafffund
Stuart J. Smyth, Peter W.B. Phillips

Bibliographic record

VenueGM crops & food · 2014
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicGenetically Modified Organisms Research
Canadian institutionsNutrasourceSaskatoon Medical ImagingUniversity of Saskatchewan
FundersGenome PrairieGenome Canada
KeywordsBiotechnologyEuropean unionGenetically modified organismGenetically modified cropsBusinessLatin AmericansInternational tradeProduction (economics)PopulationAgricultural economicsPolitical scienceBiologyEconomicsLawEnvironmental health

Abstract

fetched live from OpenAlex

The global regulation of products of biotechnology is increasingly divided. Regulatory decisions for genetically modified (GM) crops in North America are predictable and efficient, with numerous countries in Latin and South America, Australia and Asia following this lead. While it might have been possible to argue that Europe's regulations were at one time based on real concerns about minimizing risks and ensuring health and safety, it is increasingly apparent that the entire European Union (EU) regulatory system for GM crops and foods is now driven by political agendas. Countries within the EU are at odds with each other as some have commercial production of GM crops, while others refuse to even develop regulations that could provide for the commercial release of GM crops. This divide in regulatory decision-making is affecting international grain trade, creating challenges for feeding an increasing global population.

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)0.017
metaresearch head score (Gemma)0.017
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.993
Threshold uncertainty score0.089

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0170.017
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0020.002
Science and technology studies0.0070.049
Scholarly communication0.0160.016
Open science0.0020.008
Research integrity0.0300.014
Insufficient payload (model declined to judge)0.0060.001

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.

Opus teacher head0.016
GPT teacher head0.221
Teacher spread0.205 · 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.

Study designQualitative
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

Citations36
Published2014
Admission routes2
Has abstractyes

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