Trends in Approval Times for Genetically Engineered Crops in the United States and the European Union
Bibliographic record
Abstract
Abstract Genetically engineered (GE) crops are subject to regulatory oversight to ensure their safety for humans and the environment. Their approval in the European Union (EU) starts with an application in a given Member State followed by a scientific risk assessment, and ends with a political decision‐making step (risk management). In the United States (US) approval begins with a scientific (field trial) step and ends with a ‘bureaucratic’ decision‐making step. We investigate trends for the time taken for these steps and the overall time taken for approving GE crops in the US and the EU. Our results show that from 1996–2015 the overall time trend for approval in the EU decreased and then flattened off, with an overall mean completion‐time of 1,763 days. In the US in 1998 there was a break in the trend of the overall approval time. Initially, from 1988 until 1997 the trend decreased with a mean approval time of 1,321 days; from 1998–2015, the trend almost stagnated with a mean approval time of 2,467 days.
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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 | 0.005 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".