Trade in CRISPR/Gene-Edited Wheat: Partial Equilibrium Analysis.
Bibliographic record
Abstract
Previous studies have analyzed how the adoption of genetically engineered or modified technologies have affected agricultural crops such as corn, soybeans, cotton, and barley without focusing on wheat. Also, given the negative impact of drought on wheat production, no studies have focused on the implications of drought tolerant (HB4) and CRISPR/gene-editing on wheat trade. To address these issues, this study employed the partial equilibrium analysis and analyzed the implications of drought tolerant (HB4) and CRISPR/gene-editing technology adoption on wheat trade under various scenarios. The study found that when Argentina, Australia, United States, Canada, and Russia adopt gene-editing wheat, all consuming countries experience a welfare gain except Japan, Korea, Belgium, Netherland, and Italy. More so, Argentina, Mexico, Nigeria, Brazil, Egypt, and Venezuela continue to consume CRISPR wheat in all scenarios. Also, all producing countries experience a gain in producer welfare.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".