Exploration of high-density oligoarrays as tools to assess substantial equivalence of genetically modified crops
Bibliographic record
Abstract
Since the early 1990s, the concept of substantial equivalence has been a guiding principle of the Canadian Food Inspection Agency and Health Canada's regulatory approach toward products of plant biotechnology destined for the food and livestock feed markets. To assess substantial equivalence in terms of chemical composition, genetically modified (GM) plants are compared to conventional counterparts at the level of macro- and micro-nutrients, allergens and toxicants. Such targeted comparative analyses are limited in their scope and their capacity to detect unintended changes in chemical composition. There is a need to develop more effective testing protocols to improve the substantial equivalence assessment of GM crops. The objective of this thesis was to explore high-density oligoarrays as tools to assess substantial equivalence of Roundup Ready(TM) soybean. Three conventional and two GM soybean varieties were selected according to the similarity of their performance in field trials. Total RNA was extracted from first trifoliate leaves harvested from soybean plants grown in a controlled environment until the V2 stage. To annotate the 37 776 soybean probesets present on the multi-organism Soybean Affymetrix GeneChip(TM), consensus sequences were aligned with TIGR Soybean Gene Index tentative consensus sequences using BLASTN. After redefining the chip description file to exclude non-soybean probesets, the effects of three different normalization methods (Robust Multichip Average (RMA), Microarray Analysis Suite (MAS 5.0) and Model-Based Expression Index) were compared and Significance Analysis of Microarrays (SAM for R-Bioconductor) was applied to detect differential gene expression between conventional and GM soybean varieties. Eleven candidate genes were selected for further studies.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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 teacher head, 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".