Bibliographic record
Abstract
This chapter contains sections titled: Methods for Establishing 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, Oil-seed rape, Sugar Beet, Wheat, Rice, and Cotton Insect Resistance in Maize, Potatoes, Tomatoes, and Cotton Virus-resistance, Male Sterility, Delayed Fruit Ripening, and Fatty Acid Content of GMPs GM Plants “In the Pipeline” Input Traits Insect Resistance Virus, Fungal, Bacterial, and Nematode Resistance Tolerance Against Abiotic Stress Improved Agronomic Properties Traits Affecting Food Quality for Human Nutrition Increased Vitamin Content Production of Very-long-chain Polyunsaturated Fatty Acids Increased Iron Level Improved Amino Acid Composition Reduction in the Amount of Antinutritive Factors Production of “Low-calorie Sugar” 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 References
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.025 | 0.026 |
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".