Characterizing the Distribution of Ppm Gluten in Gluten Free Oatmeal Servings Contaminated with a Wheat Kernel
Bibliographic record
Abstract
Oats are often contaminated with rogue kernels of gluten-containing grains like wheat, barley and rye. When producing gluten free oatmeal, possessing an understanding of the consequences of this possibility is prudent, as labeling requirements specify a maximum amount of gluten in terms of ‘parts per million’ (ppm) gluten. Variation in contaminant kernels, along with variation due to measurement itself though, can result in a wide range of possible ppm gluten outcomes in contaminated servings. This research pursues characterization of this variability, highlighting contributors to it, doing so by quantifying distributional outcomes of ppm gluten in wheat kernel contaminated servings. This is done via statistical simulation of wheat kernel contaminated servings, done for a collection of wheat types and incorporating various measurement influences. Results indicate substantial variability in ppm gluten per serving for a given wheat type, as well as between them, with this being compounded by the measurement task itself.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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 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".