From Farming to âPharmingâ. Risks and Policy Challenges of Third Generation GM Crops
Bibliographic record
Abstract
Commercial and academic activities in the production of pharmaceuticals or other substances of industrial interests from genetically modified plants, i.e. molecular farming, have so far centred in the USA and Canada. Recent increases in EU activities and the proximity to market stage of the first plant-made pharmaceuticals, some of which from EU based companies, represent a call to action for EU regulators. Drawing on the North American debate on molecular farming it will be argued that both the rationale of and the risk issues associated with molecular farming will differ significantly from those of first generation GM crops. Based on these differences, the suitability of the existing regulatory framework, which essentially was developed in response to the arrival of insecticide and herbicide tolerant crops for food and feed use, is discussed. Possible options for adapting the already complex EU regulatory system to cater for molecular farming are examined. It will be argued that the policy challenges posed will inevitably spark a broader public debate. As an issue for debate, molecular farming is located at two crossroads: of the risk debate on agricultural biotechnology and the sustainability debate on renewables and greening of industry and of red and green biotechnology. Complex scientific, technical and legal issues, new issues at stake and a new pattern of actors are likely to give EU regulators a difficult time.
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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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.017 | 0.008 |
| Insufficient payload (model declined to judge) | 0.010 | 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".