The European Coexistence Bureau: 5 years' experience
Bibliographic record
Abstract
The European Coexistence Bureau (ECoB) was established in\n2008 to help European Union member states identify best practices\nfor technical segregation measures between GM and non-\nGM crops and, on these bases, develop guidelines for crop-specific\nmeasures for coexistence. The ECoB works with EU member\nstates’ experts, managed by the European Commission’s\nJoint Research Centre staff; the ECoB works in consultation\nwith stakeholders. The ECoB deals with best practices of coexistence\nat the technical and agronomic level, excluding related\nadministrative or legal issues. Since its establishment, the ECoB\nhas focused on coexistence in GM maize production, since this\nremains the only GM crop cultivated in EU countries. The ECoB\nhas produced Best Practice Documents (BPDs) for (i) coexistence\nof GM maize crop production with conventional and\norganic farming, (ii) monitoring efficiency of coexistence measures\nin maize crop production, and (iii) coexistence of GM\nmaize and honey production. An overview on similar efforts and\nexpert groups, which are beginning to appear outside the European\nUnion (United States, Canada, and Brazil) for development\nof coexistence guidelines, is also presented.
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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 | 0.028 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.027 | 0.008 |
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".