Environmental Effects on Allergen Levels in Commercially Grown Non-Genetically Modified Soybeans: Assessing Variation Across North America
Bibliographic record
Abstract
Soybean (Glycinemax) is a hugely valuable soft commodity that generates tens of billions of dollars annually. This value is due in part to the balanced composition of the seed which is roughly 1:2:2 oil, starch, and protein by weight. In turn, the seeds have many uses with various derivatives appearing broadly in processed food products. As is true with many edible seeds, soybeans contain proteins that are anti-nutritional factors and allergens. Soybean, along with milk, eggs, fish, crustacean shellfish, tree nuts, peanuts, and wheat, elicit a majority of food allergy reactions in the United States. Soybean seed composition can be affected by breeding, and environmental conditions (e.g., temperature, moisture, insect/pathogen load, and/or soil nutrient levels). The objective of this study was to evaluate the influence of genotype and environment on allergen and anti-nutritional proteins in soybean. To address genetic and environmental effects, four varieties of non-GM soybeans were grown in six geographically distinct regions of North America (Georgia, Iowa, Kansas, Nebraska, Ontario, and Pennsylvania). Absolute quantification of proteins by mass spectrometry can be achieved with a technique called multiple reaction monitoring (MRM), during which signals from an endogenous protein are compared to those from a synthetic heavy-labeled internal standard. Using MRM, eight allergens were absolutely quantified for each variety in each environment. Statistical analyses show that for most allergens, the effects of environment far outweigh the differences between varieties brought about by breeding.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".