Genetic transformation of crop plants: Risks and \nopportunities for the rural poor \n
Bibliographic record
Abstract
The world population is increasing at an alarming rate and is expected to increase from 6.5 billion at present to 7.5 billion by 2025. Most of this population lives in the rural areas in the developing countries where poverty, food insecurity and nutritional deficiencies are the major \n problems. Low crop productivity, limited use of inputs such as fertilizers and pesticides and \n losses due to biotic and abiotic stress factors are a major constraint to increase production and \n productivity of crops. With the advent of genetic engineering, it has become possible to clone and \n insert genes into the crop plants to confer resistance to insect pests and improve the nutritional \n quality. Genetically transformed crops with Bacillus thuringiensis and herbicide resistance genes \n have been deployed for cultivation in USA, Canada, China and Australia. However, very little has \n been done to use this technology for improving crop production in the harsh environments of the \n tropics, where the need for increasing food production is most urgent. However, there is a need to \n follow the biosafety regulations and a better presentation of the results to the general public for a \n rational deployment of the genetically transformed crops for improving the livelihoods of the rural poor. \n
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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.
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".