Bibliographic record
Abstract
New breeding techniques in plant agriculture exploded upon the scene about two years ago, in 2014. While these innovative plant breeding techniques, soon to be led by CRISPR/Cas9, initially appear to hold tremendous promise for plant breeding, if not a revolution for the industry, the question of how the products of these technologies will be regulated is rapidly becoming a key aspect of the technology's future potential. Regulation of innovative technologies and products has always lagged that of the science, but in the past decade, regulatory systems in many jurisdictions have become gridlocked as they try to regulate genetically modified (GM) crops. This regulatory incapability to efficiently assess and approve innovative new agricultural products is particularly important for new plant breeding techniques as if these techniques are classified as genetically modified breeding techniques, then their acceptance and future will diminish considerably as they will be rejected by the European Union. Conversely, if the techniques are accepted as conventional plant breeding, then the future is blindingly bright. This article examines the international debate about the regulation of new plant breeding techniques and then assesses how the Canadian regulatory system has approached the regulation of these technologies through two more public product approvals, GM apples and GM potatoes, then discusses other crop variety approval and those in the regulatory pipeline.
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 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 | 0.012 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.012 | 0.010 |
| Scholarly communication | 0.011 | 0.003 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.012 | 0.012 |
| Insufficient payload (model declined to judge) | 0.013 | 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".