Chapter 8Effects of the cultivation of genetically modied Bt crops on nontarget soil organisms
Bibliographic record
Abstract
Table 8.1 Global Area of Genetically Modied Crops in 2010: By Country (million hectares)Rank Country Area(million hectares) Biotech crops1 United States 66.8 Maize, soybean, cotton, canola, sugar beet, alfalfa, papaya, squash2 Brazil 25.4 Soybean, maize, cotton 3 Argentina 22.9 Soybean, maize, cotton 4 India 9.4 Cotton 5 Canada 8.8 Canola, maize, soybean, sugar beet 6 China 3.5 Cotton, tomato, poplar, papaya, sweet pepper 7 Paraguay 2.6 Soybean 8 Pakistan 2.4 Cotton 9 South Africa 2.2 Maize, soybean, cotton10 Uruguay 1.1 Soybean, maize 11 Bolivia 0.9 Soybean 12 Australia 0.7 Cotton, canola 13 Philippines 0.5 Maize 14 Myanmar 0.3 Cotton 15 Burkina Faso 0.3 Cotton 16 Spain 0.1 Maize 17 Mexico 0.1 Cotton, soybean 18 Columbia <0.1 Cotton 19 Chile <0.1 Maize, soybean, canola 20 Honduras <0.1 Maize 21 Portugal <0.1 Maize 22 Czech Republic <0.1 Maize, potato 23 Poland <0.1 Maize 24 Egypt <0.1 Maize 25 Slovakia <0.1 Maize 26 Costa Rica <0.1 Cotton, soybean 27 Romania <0.1 Maize 28 Sweden <0.1 Potato 29 Germany <0.1 Potatoand Saxena, 2009). Since the commercial introduction of GM plants, the acreage dedicated to GM crop cultivation has increased each year, such that the majority of all major crop plants grown in the United States-soybean, cotton, and maize-are genetically engineered (U.S. Department of Agriculture [USDA], 2010). Developing countries also continue to increase their share of global GM crop production and now account for almost half (46%) of the global hectarage of GM crops (James, 2010). This rapid and widespread adoption of GM crops has led to a dramatic shift in the agricultural landscape since the mid-1990s and has raised questions about the impact of agricultural biotechnology on nontarget organisms in the soil environment.
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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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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".