Understanding Genetically Modified Crops (GMOs): Benefits, Risks, and Future Prospects
Bibliographic record
Abstract
Genetically modified (GM) crops have revolutionized the agricultural sector by allowing scientists to rapidly introduce novel genes for desirable traits such as herbicide tolerance, pest resistance, bio-fortification, and disease resistance from one species to another, which are becoming difficult to achieve using conventional breeding methods. GM crops have improved the overall productivity of many crops, contributing positively to food security. In the past few decades, GM crops have shown significant positive effects, especially effective weed and pest management. Because of this success, GM crops in the past few decades have expanded up to 2.15 billion hectares globally. Despite these benefits, there are some serious potential issues associated with GMOs that have to be addressed such as weeds becoming resistant to herbicides due to their extensive use in GM crops, the potential threat of insects becoming resistant to CRY protein released by Bacillus thuringiensis (BT) crops, and the issues of biosafety and biodiversity of other plant and animal species linked with GM crops directly or indirectly. The review explores the basic techniques used to develop GM crops and explains the benefits of GMO crops in the field of agriculture, especially in weed management, disease resistance, and bio-fortification. And also, it highlights the prospects and examines the risks associated with GMO crops.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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 teacher head, 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".