Nutritional portrait of processed foods purchased in Québec (Canada), 2016–2022
Bibliographic record
Abstract
OBJECTIVE: The Food Quality Observatory synthetises the nutritional composition of fifteen processed food categories commonly purchased in Québec (Canada). We assessed how the new Canadian front-of-pack (FoP) labelling regulation of a ‘high in’ symbol, to be implemented as of January 1, 2026, would be potentially reflected in these categories and how simulations of reformulation would impact the presence of the symbol. DESIGN: company. Fifteen food categories have been selected, and three levels of reformulation were simulated. SETTING: The nutritional values of 5132 products were merged with sales data. 3941 products were successfully cross-referenced. RESULTS: 2336/3941) would carry the ‘high in’ symbol reflecting a high content of Na, saturated fat and/or total sugar (39 %, 16 % and 17 %, respectively). For certain food categories, a slight reduction (5–15 %) in Na, saturated fat or total sugar content would allow removing the ‘high in’ symbol in a large number of products. For example, a 5 % reduction of the Na content in sliced breads would allow 22 percentage point (pp) fewer products to display the symbol. CONCLUSIONS: This study presents a portrait of processed foods purchased in Québec (Canada) and the distribution of the FOP ‘high in’ symbol. Such a portrait generates important data to monitor the food supply’s nutritional quality, which can ultimately contribute to improving the nutritional quality of processed foods.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 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) | 0.007 | 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".