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Record W4401990658 · doi:10.1126/science.ado9340

Environmental impacts of genetically modified crops

2024· review· en· W4401990658 on OpenAlexafffund
Frederik Noack, Dennis Engist, Joséphine Gantois, Vasundhara Gaur, Batoule F. Hyjazie, Ashley Larsen, Leithen K. M’Gonigle, Anouch Missirian, Matin Qaim, Risa D. Sargent, Eduardo Souza-Rodrigues, Claire Kremen

Bibliographic record

VenueScience · 2024
Typereview
Languageen
FieldAgricultural and Biological Sciences
TopicGenetically Modified Organisms Research
Canadian institutionsUniversity of TorontoSimon Fraser UniversityUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of CanadaU.S. Department of AgricultureCanada Research ChairsSocial Sciences and Humanities Research Council of CanadaDeutsche ForschungsgemeinschaftNational Institute of Food and AgricultureNational Science Foundation
KeywordsAgricultureGenetically modified cropsBiodiversityGenetically modified organismCroppingDeforestation (computer science)PesticideEnvironmental impact assessmentHuman healthCropGeographyAgroforestryNatural resource economicsBiologyEcologyEnvironmental healthEconomics

Abstract

fetched live from OpenAlex

Genetically modified (GM) crops have been adopted by some of the world's leading agricultural nations, but the full extent of their environmental impact remains largely unknown. Although concerns regarding the direct environmental effects of GM crops have declined, GM crops have led to indirect changes in agricultural practices, including pesticide use, agricultural expansion, and cropping patterns, with profound environmental implications. Recent studies paint a nuanced picture of these environmental impacts, with mixed effects of GM crop adoption on biodiversity, deforestation, and human health that vary with the GM trait and geographic scale. New GM or gene-edited crops with different traits would likely have different environmental and human health impacts.

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.060
GPT teacher head0.322
Teacher spread0.262 · 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 designSystematic review
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

Citations58
Published2024
Admission routes2
Has abstractyes

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