GENETIC TRANSFORMATION IN AGRICULTURE: THE REAL CHANCE FOR ENSURING WORLDWIDE SUSTAINABLE FOOD SECURITY
Bibliographic record
Abstract
Obtaining genetically modified (GM) organisms, with superior biological and productive performances, represents the priority objective of modern applied genetics research, oriented towards the development of effective procedures for increasing genetic variability, according to the requirements of breeding programs of economically interesting species. GM crops can contribute essentially to the millennium development goal, that of reducing poverty and increasing food security, by optimizing agricultural productivity. GM plants have improved traits that include herbicide tolerance, disease and pest resistance, drought tolerance, health or nutritional benefits, a longer shelf life, or a more efficient industrial use. Also, the GM crops contribute to sustainable environmental protection by reduction of the pesticides application amount and reduction of CO2 emissions. In this context, the aim of the present paper is the worldwide brief radiography of transgenesis, in terms of surfaces, the main producing countries, but also of the main GM crops and their market. The research method consisted in selecting of several scientific results from the WOS, Clarivate Analytics, Scopus and Springer databases. Also, were accessed several statistical data of the ISAAA, USDA, Research and Market, MADR, etc. The main producing countries of GM crops are USA, Brazil, Argentina, Canada, India, Paraguay and China. The largest GM areas are occupied by soybean, maize, cotton, canola and alfalfa. The global agricultural biotechnology market for transgenic crops is expected to reach 12.07 billion USD in 2026, growing by 18.2% compared with 2021. Genetic transformation and GM agricultural crops represent an effective strategy and real chance to counteract climate change and food insecurity, and recently developed genetic engineering techniques will play an important role in the future.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".