Proposed criteria for identifying GE crop plants that pose a low or negligible risk to the environment under conditions of low-level presence in seed
Bibliographic record
Abstract
The low-level presence (LLP) of genetically engineered (GE) seeds that have been approved in the country of origin but not the country of import presents challenges for regulators in both seed importing and exporting countries, as well as for the international seed trade and the farmers who rely on it. In addition to legal, financial and regulatory challenges, such LLP situations in seed may also require an environmental risk assessment by the country of import. Such assessments have typically been informed by the national framework established to support decisions related to wide scale cultivation, and frequently do not take into account the low environmental exposure and prior regulatory history of the GE plant. In addition, such assessment processes may not be well suited to the decision-making timeframe that is necessary when dealing with an LLP situation in imported seed. In order to facilitate regulatory decision making, this paper proposes a set of scientific criteria for identifying GE crop plants that are expected to pose a low or negligible risk to the environment under LLP conditions in seed. Regulatory decision makers in some importing countries may decide to use these criteria to assist in risk analysis associated with LLP situations they are experiencing or could experience in the future, and might choose to proactively apply the criteria to identify existing GE plants with regulatory approvals in other countries that would be expected to pose low risk under conditions of LLP in seed.
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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 | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.009 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".