Using the GENESYS model quantifying the effect of cropping systems on gene escape from GM rape varieties to evaluate and design cropping systems
Bibliographic record
Abstract
\n\t\t\t\tGene flow in rapeseed is a process taking place both in space and over the years and cannot be studied exclusively by field trials. Consequently, the GENESYS model was developed to quantify the effects of cropping systems on transgene escape from rapeseed crops to rapeseed volunteers in neighbour plots and in the subsequent crops. In the present work, this model was used to evaluate the risk of rape harvest contamination by extraneous genes in various farming systems in case of co-existing GM, conventional and organic crops. When 50 % of the rape varieties in the region were transgenic, the rate of GM seeds in non-GM crop harvests on farms with large fields was lower than the 0.9 % purity threshold proposed by the EC for rape crop production (food and feed) harvests, but on farms with smaller fields, the threshold was exceeded. Harvest impurity increased in organic farms, mainly because of their small field size. The model was then used to evaluate the consequences of changes in farming practices and to identify those changes reducing harvest contamination. The effects of these changes depended on the field pattern and farming system. The most efficient practices in limiting harvest impurity comprised improved set-aside management by sowing a cover crop in spring on all set-aside fields in the region, permanently banning rape crops and set-aside around seed production fields and (for non-GM farmers) clustering farm fields to reduce gene inflow from neighbour fields.\n\t\t\t
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".