A Framework for Postrelease Environmental Monitoring of Second‐generation Crops with Novel Traits
Bibliographic record
Abstract
ABSTRACT As first‐generation genetically modified/transgenic crops with novel agronomic traits have been grown commercially in a number of countries since the mid‐1990s, second‐generation crops with novel traits (CNTs) are now being tested in confined field trials around the world. Postrelease monitoring (PRM) of abiotic stress–tolerant and other second‐generation CNTs will strengthen prerelease environmental risk assessments, for which protocols are being developed. We outline a comprehensive framework and protocol for case‐specific PRM of such CNTs in Canada, using drought‐tolerant canola (Brassica napus L.) as a model CNT. The primary potential environmental risk associated with cultivation of drought‐tolerant canola is increased invasiveness of volunteers or feral plants (self‐perpetuating populations) and weedy relative–crop hybrids or backcrossed progeny in ruderal (noncropped disturbed) and natural areas adjacent to CNT cultivation, resulting in loss of abundance or biodiversity of native plant species. Accurately predicting CNT invasiveness a priori is problematic, especially for traits that may enhance plant fitness and invasiveness. Thus, PRM can effectively address the greater uncertainties in the environmental risk assessment of these second‐generation vs. first‐generation CNTs and thereby enhance environmental protection and security of the food supply.
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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 | 0.015 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".