The GMO Pipeline, Import Bans and Asynchronous Approvals – The Impact on Agricultural Trade
Bibliographic record
Abstract
Abstract The divergence in approaches to regulation of genetically modified organisms (GMOs) among countries is a contentious policy issue that has externalities that inhibit trade in agricultural products. One result is that approvals of GMO events is internationally asynchronous. Countries whose approval processes are slower or more stringent often impose import bans or other non-tariff barriers on imports of GM products they have rejected or have yet to approve. These trade barriers have been the subject of considerable investigation. As more and more GMO events are approved in some countries, but not in others, the probability of unintended mingling of GM crops in shipments of non-GM crops increases. Shipments of non-GM crops with a low level presence of non approved GM crops are routinely rejected by importing countries. This growing disruption to trade has not received a great deal of attention. This paper uses information from the GMO research and commercialization pipeline to estimate the potential impact of increases in mingling on international trade flows using a CGE model – GTAP. The results suggest that the growing potential for mingling will have a considerable detrimental impact on trade flows and, hence, tolerance levels should be an important question for multilateral trade negotiations.
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.000 |
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".