Zero Tolerance for<scp>GM</scp>Flax and the Rules of Trade
Bibliographic record
Abstract
Abstract Trade in genetically modified products is a longstanding and contentious issue in agricultural trade. One issue has not, as yet, received much attention. This is the mingling of unapprovedGMproducts with conventional products. This issue is likely to gain more prominence in the future as newGMproduct development accelerates. Until recently, problems with mingling were largely one‐off events. Recently, however, an ongoing case of mingling has arisen – the case of CanadianGMflax in theEU. The case led to an embargo of imports from Canada and subsequently the bilateral negotiation of a protocol to allow exports to resume. The case raises a number of important issues pertaining to the objective of zero tolerance policies forGMproducts, the operationalisation of zero tolerance, the role of the testing industry, the design of testing regimes and the risks associated with the absence of transparency and/or international standardisation. It is concluded that mingling is a topic that is deserving of multilateral attention.
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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.022 |
| Scholarly communication | 0.015 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".