Genetic Transformation of Crops for Insect Resistance: Potential and Limitations
Bibliographic record
Abstract
Transgenic resistance to insects has been demonstrated in plants expressing insecticidal genes such as δ -endotoxins from Bacillus thuringiensis (Bt), protease inhibitors, enzymes, secondary plant metabolites, and plant lectins. While transgenic plants with introduced Bt genes have been deployed in several crops on a global scale, the alternative genes have received considerably less attention. The protease inhibitor and lectin genes largely affect insect growth and development and, in most instances, do not result in insect mortality. The effective concentrations of these proteins are much greater than the Bt toxin proteins. Therefore, the potential of some of the alternative genes can only be realized by deploying them in combination with conventional host plant resistance and Bt genes. Genes conferring resistance to insects can also be deployed as multilines or synthetic varieties. Initial indications from deployment of transgenics with insect resistance in diverse cropping systems in USA, Canada, Argentina, China, India, Australia, and South Africa suggest that single transgene products in standard cultivar backgrounds are not a recipe for sustainable pest management. Instead, a much more complex approach may be needed, one which may involve deployment of a combination of different transgenes in different backgrounds. Under diverse climatic conditions and cropping systems of tropics, the success in the utilization of transgenics for pest management may involve decentralized national breeding programs and several small-scale seed companies. While several transgenic crops with insecticidal genes have been introduced in the temperate regions, very little has been done to use this technology for improving crop productivity in the harsh environments of the tropics, where the need for increasing food production is most urgent. There is a need to develop appropriate strategies for deployment of transgenics for pest management, keeping in view the pest spectrum involved, and the effects on nontarget organisms in the ecosystem.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".