The use of whole food animal studies in the safety assessment of genetically modified crops: Limitations and recommendations
Bibliographic record
Abstract
There is disagreement internationally across major regulatory jurisdictions on the relevance and utility of whole food (WF) toxicity studies on GM crops, with no harmonization of data or regulatory requirements. The scientific value, and therefore animal ethics, of WF studies on GM crops is a matter addressable from the wealth of data available on commercialized GM crops and WF studies on irradiated foods. We reviewed available GM crop WF studies and considered the extent to which they add to the information from agronomic and compositional analyses. No WF toxicity study was identified that convincingly demonstrated toxicological concern or that called into question the adequacy, sufficiency, and reliability of safety assessments based on crop molecular characterization, transgene source, agronomic characteristics, and/or compositional analysis of the GM crop and its near-isogenic line. Predictions of safety based on crop genetics and compositional analyses have provided complete concordance with the results of well-conducted animal testing. However, this concordance is primarily due to the improbability of de novo generation of toxic substances in crop plants using genetic engineering practices and due to the weakness of WF toxicity studies in general. Thus, based on the comparative robustness and reliability of compositional and agronomic considerations and on the absence of any scientific basis for a significant potential for de novo generation of toxicologically significant compositional alterations as a sole result of transgene insertion, the conclusion of this review is that WF animal toxicity studies are unnecessary and scientifically unjustifiable.
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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 | 0.047 | 0.055 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".