Peanut and hazelnut occurrence as allergens in foodstuffs with precautionary allergen labeling in Canada
Bibliographic record
Abstract
Precautionary allergen labeling (PAL) is widely used by food industries. Occurrence studies revealed that few analyzed products contained the allergen(s) present in the statement, but little is known in Canada. To improve manufacturing practices and better manage allergen cross-contamination, occurrence data is needed to determine the exposure of allergic individuals eating those products. Samples were analyzed for peanuts (n = 871) and hazelnuts (n = 863) using ELISA methods. Within samples analyzed for peanuts, 72% had a PAL (n = 628), 1% had peanuts as a minor ingredient (n = 9) and 27% were claimed "peanut-free" (n = 234). Most hazelnut samples had a PAL for tree nuts/hazelnuts (94%; n = 807) with 6% claimed "nut-free" (n = 56). Peanuts and hazelnuts were found in 4% (0.6-28.1 ppm) and 9% (0.4-2167 ppm) of all samples, respectively. Chocolates were mostly impacted; they should be treated apart from other foods and used in risk assessments scenarios to improve manufacturing practices, reducing unnecessary PAL use.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".