Biotech crop planting resumes high adoption in 2016
Bibliographic record
Abstract
The global area of biotech crops in 2016 increased from 179.7 million hectares to 185.1 million hectares, a 3% increase equivalent to 5.4 million hectares. Some 26 countries planted biotech crops, 19 of which were developing countries and seven were industrial. Information and data collected from various credible sources showed variations from the previous year. Fluctuations in biotech crop area (both increases and decreases) are influenced by factors including, among others, acceptance and commercialization of new products, demand for meat and livestock feeds, weather conditions, global market price, disease/pest pressure, and government's enabling policies. Countries which have increased biotech crop area in decreasing order in 2016 were Brazil, United States of America, Canada, South Africa, Australia, Bolivia, Philippines, Spain, Vietnam, Bangladesh, Colombia, Honduras, Chile, Sudan, Slovakia, and Costa Rica. Countries with decreased biotech area in decreasing order were China, India, Argentina, Paraguay, Uruguay, Mexico, Portugal, and Czech Republic, in decreasing incremental decrease in biotech area. Pakistan and Myanmar were the only countries with no change in biotech crop (cotton) planted. Information detailed in the paper including future crops and traits in each country could guide stakeholders in informed crafting of strategies and policies for increased adoption of biotech crops in the country.
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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 | 0.000 |
| 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 | 0.001 |
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 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".