Modeling the Influence of Gene Flow and Selection Pressure on the Frequency of a GE Herbicide‐Tolerant Trait in Non‐GE Wheat and Wheat Volunteers
Bibliographic record
Abstract
Different types of transgenic wheat (Triticum aestivum L.) will be ready for commercialization within the next decade, including varieties with higher yields, greater tolerance to biotic and abiotic stresses, and resistance to herbicides. The release of genetically engineered (GE) wheat may require segregation of GE and non‐GE wheat to satisfy international markets. Before GE wheat is released, it is important to understand the movement of a GE trait within the agronomic production system. This study evaluated the effects of gene flow and selection pressure on the frequency of a GE trait (herbicide tolerance) in non‐GE wheat and wheat volunteers. Gene flow of GE traits to non‐GE wheat is inevitable through pollen or seed movement. When a GE trait does not confer a selective advantage in the production system, the frequency of the GE trait within non‐GE wheat will be a function of the rate of gene flow. Low rates of gene flow will lead to low levels of the GE trait in the non‐GE crop. With repeated gene flow events, the frequency of the GE trait may slowly increase in the non‐GE crop. When the GE trait has a selective advantage, the frequency of the GE trait will increase rapidly in volunteer populations of the non‐GE crop. Herbicide tolerance is an example of a GE trait that provides a high selective advantage when the herbicide is applied in the production system. Predictive models show that even with very low rates of initial gene flow, frequent applications of a highly effective herbicide will quickly increase the frequency of the herbicide‐tolerant (HT) GE trait in volunteer populations. This has negative implications for control of volunteers and the ability to maintain tolerance levels of GE traits in non‐GE wheat crops.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".