Bibliographic record
Abstract
Several GM cultivars of cereal crops have been developed, but maize is the only GM cereal crop being grown internationally in commercially significant quantities. The main producing countries are the USA, Brazil, Argentina, Canada and India. Some countries have approved GM cultivars of maize for importation only and not for cultivation. All of the approved cultivars have received clearance by regulatory authorities, based on safety tests and on substantial equivalence with conventional lines of maize. Globally, about 47 million hectares (equivalent to 27% of the 175 million hectares) were of stacked type in 2013. A large body of data published in peer-reviewed scientific journals shows that no safety concerns are evident when GM lines of maize are included in the diet of test animals. Reports from one laboratory of the possibility of cancer induction as a result of consuming GM maize have been discredited. The available data indicate that cereal grains contain chemical and pesticide residues, but that the amounts are too small to be of concern in relation to human health. Many of the residues found are at the limit of detection. No separate datasets on residue levels in GM grains are currently publicly available: more studies need to be carried out to provide comparable data on GM and conventionally grown grains. The published data indicate a lower level of mycotoxins in GM maize possessing any of the traits for insect resistance. The overall conclusion that can be drawn from the available data is that GM and conventional cereal crops are of similar nutritive value, except in those cases where the GM cultivar has an enhanced nutritional feature such as increased lysine content. No studies appear to have been conducted on the organoleptic qualities of GM maize relative to conventionally grown maize or other cereal crops.
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) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.158 | 0.115 |
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".