Bibliographic record
Abstract
Global status and progress of commercialized transgenic crops were reviewed.The acreage of transgenic crops increased rapidly since 1996 and reached 148 million hectares in 2010,which was equivalent to 10% of the total acreage of all crops in the world,and the 87-fold of 1.7 million hectares in 1996.Ten million hectares increased every year.The acreage of transgenic crops reached 1 097 million hectares from 1996 to 2010,and reached 521 million hectares in recent 4 years that was the proximal amount of acreage from 1996 to 2006.Number of countries planting transgenic crops soared to a record 29 in 2010.In addition,30 countries imported transgenic crop for food,feed and environmental release whose population was 75% of the total population of world.Transgenic crops were mainly planted in USA(45.1%),Brazil(17.2%),Argentina(15.5%),India(6.4%)and Canada(5.9%).24 kinds of plants were approved for planting and soybean(49.5%),maize(31.6%),cotton(14.2%) and rapeseed(4.7%) were mostly important transgenic crops and with high adoption rates of 81%,64%,29% and 23% respestively.Transgenic traits were mainly herbicide tolerance(61%),stacked traits(22%) and insect resistance(17%).
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".