A comparison of the fat composition and prices of margarines between 2002 and 2006, when new Canadian labelling regulations came into effect
Bibliographic record
Abstract
OBJECTIVE: To examine the effect of the new Canadian labelling regulations on the fat composition and prices of margarines. STUDY DESIGN: A survey of all margarines sold in major supermarkets in the Greater Toronto area was conducted in 2006, and results were compared with those of a similar survey conducted in 2002. Average fat composition, proportion of 'trans fat-free' margarines and average prices of margarines were compared. A general linear model procedure was used to compare the relationship between price and fat composition in 2002 and 2006. RESULTS: Average amounts of trans fatty acids (TFA) and MUFA decreased, while average amounts of PUFA increased significantly from 2002 to 2006. The proportion of margarines with less than 0.2 g TFA/10 g serving rose significantly from 31 % in 2002 to 69 % in 2006. Margarines lower in TFA on average cost significantly more than margarines with greater amounts of these fats, and this relationship appeared stronger in 2006 relative to 2002. CONCLUSIONS: There is evidence of reductions in TFA in margarines since new labelling regulations came into effect in Canada; however, TFA reductions appeared to be restricted to higher-priced margarines. Results suggest that voluntary approaches (i.e. manufacturer incentives via labelling) to reduce population intakes of TFA will yield little changes in TFA content of low-cost products and thus may have limited benefit for lower-income groups, who are at higher risk of heart disease.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".