Characterizing the Distribution of Ppm Gluten in Gluten Free Oatmeal Servings Contaminated with a Barley Kernel
Bibliographic record
Abstract
Oats are regularly contaminated with gluten-containing grains like wheat, barley and rye. For producers of gluten free oatmeal, contamination potential makes it prudent to understand the consequences in terms of gluten dosing, as labeling requirements specify a gluten maximum. To do this, statistical simulation has been used to produce virtual oat servings (40g) contaminated with either two row or six row spring barley. The results are probability distributions for ‘actual’ ppm gluten (free of measurement influences) and ‘as measured’ via R5 ELISA. Findings show ‘actual’ ppm gluten to be normally distributed with 57 ppm and 41 ppm gluten averages with 14 and 12 ppm standard deviations (stdevs) for two and six row barley, respectively. ‘As measured’ ppm gluten results are lognormally distributed with 61 and 44 ppm gluten averages with 63 and 47 stdevs for two and six row barley, respectively, employing an 80% analytical recovery rate and a multiplier of 1. These analyses show that ‘as measured’ results possess false negative probabilities (relative to a < 20 ppm gluten regulatory requirement) from 0.14 to 0.34 depending on recovery rate and barley type. This work highlights the need for non-homogenous grinding issues to be addressed in whole grain gluten assessment, for analytical recovery rates to be defined for gliadin in oats, and for appropriate conversion factors to be determined in order to attain capable measurement of gluten in oats due to barley kernel contamination.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".