Identifying GM crops for cultivation in the EU through a Delphi forecasting
Bibliographic record
Abstract
This paper reports the design and implementation of a Delphi forecasting exercise carried out to identify crop traits that could feasibly be introduced to the advantage of European arable farmers, and for the general benefit of members of the public in EU member states. An expert stakeholder panel was recruited, and in the first round of the consultation, asked for opinions regarding a number of scenarios concerning the availability of GM events, and also scenarios that envisage novel crops developed using advanced technology that is not classified as GM. In a second round of consultation, panel members were asked to comment anonymously on opinions elicited in the first phase. Preliminary results indicate that crops with input traits most likely to become available in EU before 2025 are HTIR maize, HT sugarbeet and HT soybean; these crops are already widely adopted outside Europe. The crops with output traits most likely to become available are winter-sown varieties of rape with reduced saturated fats, spring varieties of which are already available outside EU (notably Canadian Canola).
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 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 | 0.005 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".