Progress towards eliminating industrially produced trans-fatty acids in the Canadian marketplace, 2013–2017
Bibliographic record
Abstract
OBJECTIVE: To assess the prevalence of partially hydrogenated oils (PHO), hydrogenated oils (HO) and/or both in Canadian packaged foods in 2013 and 2017 and to determine the mean trans-fatty acid (TFA) content of products declaring such oils. DESIGN: Repeated cross-sectional study of the Food Label Information Program. SETTING: Food labels (n 32 875) were collected from top Canadian grocery retailers in 2013 and 2017. Proportions of products declaring PHO, HO and/or both in the Ingredients List were calculated by year and food category. The percentage contribution of TFA (g) to total fat (g) was calculated and compared against the voluntary TFA limits, defined as <2 % of total fat content for fats and oils, and <5 % for all other foods. Foods exceeding limits were identified. The mean TFA content (in g/serving and per 100 g) was calculated for products with these oils. RESULTS: The use of PHO, HO and/or both significantly decreased in Canadian foods from 2013 to 2017 (0·8 to 0·2 %, 5 to 2·4 % and 5·7 to 2·6 %, respectively, for PHO, HO and/or both). The mean TFA content of products containing PHO increased (0·34 to 0·57 g TFA/serving); although it was not statistically significant, it is still concerning that TFA content increased. The TFA content significantly decreased in foods with HO (0·24 to 0·16 g TFA/serving, P < 0·05) during 2013-2017. CONCLUSIONS: Products with PHO continue to be present in the Canadian marketplace, despite voluntary efforts to eliminate them. Products with HO should also be monitored, as they can also contribute to TFA content in 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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".