Agriculture Under Threat — A Crisis of Confidence? The Solution: Redefine Adventitious Presence Maximum Levels from Zero to Zero
Bibliographic record
Abstract
The issue of Adventitious Presence (AP) of genes, those that are not "naturally" present in food and crops but rather have been placed there using recombinant deoxyribonucleic acid (DNA) technology, has become a hot issue for producers and consumers. It can also be a major problem for exporters. Part of this problem is the reality that zero presence is now impossible to guarantee in some crops and products. Pressure has arisen to establish a Low Level Presence (LLP) threshold, one that is above zero, to be determined at an international level. This would allow crops to be imported and exported without the AP genes being approved in the importing country if they are approved in another country. The reality of biotechnological innovation in crops is that it is inevitable that there will be gene "flow" between varieties. This article examines the background of AP, the current state of policy and legislation, and why this has become contentious for producers, importers and exporters. This article examines the Canadian position towards AP as an illustration of a nation that produces many agricultural products based on genetically modified crops.
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 | 0.016 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.044 |
| Scholarly communication | 0.013 | 0.015 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.018 | 0.029 |
| Insufficient payload (model declined to judge) | 0.007 | 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".