ANALYSIS OF GLOBAL TRENDS IN GM MAIZE APPROVALS IN THE PERIOD 2014-2018
Bibliographic record
Abstract
GM maize events are developed for the benefit of the global population but their approval in each country varies according to consumer acceptance, needs and commercial interests. This study analyzed the number of global approvals (by country and type of approval) for GM maize in the period 2014-2018, based on statistical data collected from the ISAAA-GM Approval Database. Descriptive statistics, regression equations and coefficient of determination were used to identify the trends for these indicators. The results showed that a total of 691 applications were approved in 28 countries. In 2016 was registered the highest number of food, feed and cultivation approvals. South Korea, Argentina, Brazil and Taiwan were the top four countries with the most food approvals, Brazil, South Korea, Argentina and Japan were the top four countries with the most feed approvals, and Argentina, Brazil, Japan and Canada were the top four countries that issued the most cultivation approvals. In developing country the rate of acceptance for GM maize was higher compared to developed countries. Europe issued a total of 26 approvals only for food and feed, with most approvals being issued in 2016.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".