Bibliographic record
Abstract
Our comprehensive database, aligned with the International Service for the Acquisition of Agri-Biotech Applications (ISAAA) standards, provides a systematic overview of genetically modified crops approved globally. The database catalogs each of 32 approved GMO crops, detailing the specific traits acquired through genetic modification and the authorized uses for each crop. The acquired traits range from abiotic stress tolerance, altered growth/yield, disease resistance, and herbicide tolerance to insect resistance, modified product quality, nematode resistance, and even intricate pollination control systems. Equally significant, the authorized uses categorize each GMO according to its approved applications, such as food consumption, animal feed, and cultivation practices. This organized and comprehensive approach allows stakeholders, researchers, and policymakers to access valuable information, facilitating informed decision-making regarding the deployment and regulation of genetically modified crops on a global scale in 46 countries: Argentina, Australia, Bangladesh, Bolivia, Brazil, Burkina Faso, Canada, Colombia, Costa Rica, Cuba, Egypt, Eswatini, Ethiopia, European Union, Ghana, Honduras, Chile, China, India, Indonesia, Iran, Japan, Kenya, Malaysia, Mexico, Myanmar, New Zealand, Nigeria, Norway, Pakistan, Panama, Paraguay, Philippines, Russia, Singapore, South Africa, South Korea, Sudan, Switzerland, Taiwan, Thailand, Turkey, United States, Uruguay, Vietnam, Zambia.
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 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 0.010 |
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; both teacher heads agree on what is shown here.
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".