Bibliographic record
Abstract
A caveat must be addressed at the start of this annual review of international biotechnology law and policy. Due to technical problems experienced by the publisher, fully beyond the author’s control, the report of developments during 2010 unfortunately was not published in last year’s volume of this Yearbook. As a consequence, since the year 2010 saw some important milestones for international biotechnology law and policy, the following report will cover developments during both 2010 and 2011. The year 2010 marked the fifteenth anniversary of the commercialization of genetically modified (GM) crops. And the anniversary year was one of record achievements, according to the annual Report on the Global Status of Commercialized Biotech/GM Crops by the International Service for the Acquisition of Agri-biotech Applications. In the first fifteen years since the first commercial transgenic plants were planted in 1996, the worldwide-accumulated hectarage of GM crops has surpassed 1 billion hectares (further growing to 1.25 billion hectares by 2011, which is equivalent to an area 25 percent larger than the total land mass of the United States or China). The record number of 148 million hectares planted in 2010 (and almost 160 million hectares in 2011) translates to an eighty-seven-fold increase over the premiere season in 1996 (approximately 94-fold by 2011), and it was planted by a record 15.4 million farmers worldwide (almost 16.7 million in 2011). The number of countries planting biotech crops has also reached an all-time high of twenty-nine (which has nearly quintupled from the initial six in 1996), including Pakistan, Myanmar, and Sweden as new participants, and Germany, which resumed GM planting after a brief stop. Of the total twenty-nine GM-growing countries, nineteen are developing nations. For the first time, in both 2010 and 2011, the top ten countries each grew more than one million hectares, notably the United States (2010: 66.8 million hectares; 2011: 69 million hectares), Brazil (2010: 25.4 million hectares; 2011: 30.3 million hectares), Argentina (2010: 22.9 million hectares; 2011: 23.7 million hectares), India (2010: 9.4 million hectares; 2011: 10.6 million hectares), Canada (2010: 8.8 million hectares; 2011: 10.4 million hectares), China (2010: 3.5 million hectares; 2011: 3.9 million hectares), Paraguay (2010: 2.6 million hectares; 2011: 2.8 million hectares), Pakistan (2010: 2.4 million hectares; 2011: 2.6 million hectares), South Africa (2010: 2.2 million hectares; 2011: 2.3 million hectares), and Uruguay (2010: 1.1 million hectares; 2011: 1.3 million hectares).
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.156 | 0.156 |
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".