Is genetically modified crop the answer for the next green revolution?
Bibliographic record
Abstract
Post-green revolution advances made in biotechnology paved the way of cultivating the high-yielding, stress and disease resistant genetically modified (GM) varieties of wheat, rice, maize cotton and several other crops. The recent rapid commercialization of the genetically modified crops in Asia, Americas and Australia indicates the potentiality of this new technology. GM crops give higher yields and are rich in nutritional values containing vitamins and minerals and can thus can help to alleviate hunger and malnutrition of the growing population in the under developed and developing countries. It could also be possible to develop more biotic and abiotic stress resistant genotypes in these crops where it was difficult to develop due to the unavailability of genes of resistance in the crossing germplasms. However, further research and investigations are needed to popularize the cultivation of these crops in different parts of the world. This review provides an insight of the impact of GM crops on contemporary agriculture across the past few decades, traces its' history across time, highlights new achievements and breakthroughs and discusses the future implication of this powerful technology in the coming few decades.
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 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".