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Record W3005614734 · doi:10.1016/j.tibtech.2019.12.002

Options to Reform the European Union Legislation on GMOs: Scope and Definitions

2020· article· en· W3005614734 on OpenAlexaboutno aff
Dennis Eriksson, René Custers, Karin Edvardsson Björnberg, Sven Ove Hansson, Kai Purnhagen, Matin Qaim, Jörg Romeis, Joachim Schiemann, Stephan Schleissing, Jale Tosun, Richard G. F. Visser

Bibliographic record

VenueTrends in biotechnology · 2020
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicGenetically Modified Organisms Research
Canadian institutionsnot available
FundersBundesministerium für Bildung und ForschungStiftelsen för Miljöstrategisk Forskning
KeywordsScope (computer science)LegislationEuropean unionPolitical scienceCoherence (philosophical gambling strategy)Genetically modified organismBusinessLaw and economicsInternational tradeLawEconomicsComputer scienceBiology

Abstract

fetched live from OpenAlex

We discuss options to reform the EU genetically modified organisms (GMO) regulatory framework, make risk assessment and decision-making more consistent with scientific principles, and lay the groundwork for international coherence. The first in a three-part series, this article focuses on reform options related to the scope of the legislation and the GMO definition.

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)0.092
metaresearch head score (Gemma)0.102
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.092
Threshold uncertainty score0.488

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0920.102
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0020.004
Bibliometrics0.0040.004
Science and technology studies0.0050.021
Scholarly communication0.0190.030
Open science0.0070.013
Research integrity0.0440.022
Insufficient payload (model declined to judge)0.0070.002

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 head0.093
GPT teacher head0.273
Teacher spread0.180 · 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 designTheoretical or conceptual
Domainnot available
GenreMethods

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

Citations62
Published2020
Admission routes1
Has abstractyes

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