Bibliographic record
Abstract
Methods to Establish Genetically Modified Plants Transformation Methods Agrobacterium transformation Direct gene transfer Tissue Requirements Molecular Requirements Promoter Codon usage Selectable marker and reporter genes GM Plants Already on the Market (EU, USA, Canada, Japan) Herbicide Resistance in Soybean, Maize, Oilseed rape, Sugar beet, Rice, and Cotton Insect Resistance in Maize, Potatoes, Tomatoes, and Cotton Virus-resistance, Male Sterility, Delayed Fruit Ripening, and Fatty Acid Contents in GMPs GM Plants “in the Pipeline ” Input Traits Insect resistance in rice, soybean, oilseed rape, eggplant, walnut, grape, and peanut Disease resistance in maize, potatoes, fruits, and vegetables Tolerance against abiotic stresses Improved agronomic properties Traits Affecting Food Quality for Human Nutrition Increased carotenoid content in rice and tomato Elevated iron level in rice and wheat Improved amino acid composition in potato plants Reduction in the content of antinutritive factors in cassava Production of “low-calorie sugar” in sugar beet Seedless fruits and vegetables Traits that Affect Processing Altered gluten level in wheat to change baking quality Altered grain composition in barley to improve malting quality Traits of Pharmaceutical Interest Production of vaccines Production of pharmaceuticals Outlook
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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 0.011 |
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".