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Record W4280546757 · doi:10.1155/2022/3489785

Labeling of Genetically Modified (GM) Foods in Peru: Current Dogma and Insights of the Regulatory and Legal Statutes

2022· review· en· W4280546757 on OpenAlexaff
Jaime Delgado-Zegarra, Aldo Álvarez-Risco, Carmen Cárdenas, Massiel Donoso, Stephanie Moscoso, Brenda Rojas Román, Shyla Del-Aguila-Arcentales, Neal M. Davies, Jaime A. Yáñez

Bibliographic record

VenueInternational Journal of Food Science · 2022
Typereview
Languageen
FieldAgricultural and Biological Sciences
TopicGenetically Modified Organisms Research
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsTransformative learningStatuteLegislatureGenetically modified organismCoronavirus disease 2019 (COVID-19)BusinessPandemicGenetically modified foodFood securityClosure (psychology)Public economicsPolitical scienceBiotechnologyEconomicsMedicineBiologyLawDiseasePsychologyAgriculture

Abstract

fetched live from OpenAlex

The COVID-19 pandemic has affected and afflicted human lives and been a transformative catalyst leading to closure of many companies, disrupting mental health, and reducing access to food and exacerbating food insecurity. This presents an opportunity to reflect on and examine genetically modified (GM) foods and their effective legislative regulation for the benefit of consumers. This review presents a detailed analysis of GM foods' regulation in Peru and the analysis of certain specific cases that show the need for greater regulation of the industry.

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)0.001
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: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.975
Threshold uncertainty score0.272

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.001
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 head0.080
GPT teacher head0.332
Teacher spread0.252 · 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 designOther design
Domainnot available
GenreReview

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

Citations19
Published2022
Admission routes1
Has abstractyes

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