<i>The Relationship Between Price, Amounts of Saturated and Trans Fats, and</i> Nutrient Content Claims on Margarines and Oils
Bibliographic record
Abstract
PURPOSE: Modifications to the amount and type of fat in the diet are recommended as strategies to help reduce heart disease risk. Individuals can choose from a variety of margarines and oils to alter their intakes of different types of fats, and nutrient content claims on product labels (e.g., 'low in saturated fat') can help them quickly identify healthful products. However, margarines and oils vary in price. METHODS: To examine the relationship between the price and amounts of saturated and trans fats in margarines and oils, and the relationship between price and the presence of nutrient content claims, price and label information were recorded for margarines (n=229) and oils (n=342) sold in the major supermarkets within the Greater Toronto Area. RESULTS: Linear regression analysis revealed a negative relationship between the price and amounts of saturated fat and trans fats in margarines, but not in oils. Margarines with a nutrient content claim were significantly more expensive than were those without a claim. CONCLUSIONS: The findings for margarines are of particular concern for lower income groups for whom budgetary constraints result in the purchase of lower priced foods, and also raise important questions about the usefulness of nutrient content claims in guiding food selections.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".