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Record W4411037159 · doi:10.51470/psr.2025.06.01.24

Advancing Agricultural Productivity and Sustainability Through Genetically Modified Crops and Modern Biotechnology

2025· article· en· W4411037159 on OpenAlexaboutno aff
M Anupama

Bibliographic record

VenuePlant Science Review · 2025
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicGenetically Modified Organisms Research
Canadian institutionsnot available
Fundersnot available
KeywordsSustainabilityProductivityAgricultural biotechnologyAgricultureGenetically modified cropsGenetically modified organismBiotechnologyAgricultural economicsBusinessBiologyEconomicsTransgeneEcologyEconomic growth

Abstract

fetched live from OpenAlex

Agricultural biotechnology plays a pivotal role in enhancing crop productivity, improving resistance to pests and diseases, and ensuring food security through the development of genetically modified (GM) crops. This review explores the science behind GM technology, focusing on the transfer and manipulation of specific genes to impart desirable traits such as herbicide tolerance, insect resistance, and drought resilience. It highlights the major genetically modified crops, including soybean, maize, and cotton, and their adoption in leading countries such as the USA, Brazil, Argentina, India, and Canada. The paper also discusses the potential benefits, risks, and regulatory considerations associated with GM crops. Overall, modern biotechnology offers promising avenues for sustainable agriculture, although it requires balanced assessment to address ecological and socio-economic concerns.

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)0.001
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.018
GPT teacher head0.278
Teacher spread0.260 · 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 designNot applicable
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

Citations1
Published2025
Admission routes1
Has abstractyes

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