Changes in fat contents of US snack foods in response to mandatory <i>trans</i> fat labelling
Bibliographic record
Abstract
OBJECTIVE: Impact of mandatory trans fat labelling on US snack food introductions is examined. DESIGN: Using label information, lipid ingredients and fat profiles are compared pre- and post-labelling. SETTING: Key products in the US snack food industry contribute significant amounts of artificial trans fat. Industry efforts to reformulate products to lower trans fat may alter the overall fat profile, in particular saturates. SUBJECTS: Composition data for more than 5000 chip and cookie products introduced for sale between 2001 (pre-labelling) and 2009 (post-labelling) were analysed. RESULTS: One-way ANOVA was used to test for significant changes in saturated fat content per serving and the ratio of saturated to total fat. The shares of chip and cookie introductions containing partially hydrogenated vegetable oil declined by 45 and 42 percentage points, respectively. In cookies, there was an increase of 0·49 (98 % CI 0·01, 0·98) g in the average saturated fat content per 30 g serving and an increase of 9 (98 % CI 3, 15) % in the average ratio of saturated to total fat. No statistically significant changes in fat content were observed in chips. CONCLUSIONS: This research suggests that, holding other factors constant, the policy has resulted in a decreased use of partially hydrogenated vegetable oil in chip products without a corresponding increase in saturated fat content, but led to significantly higher levels of saturated fat and ratio of saturated fat to total fat in cookie products.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.002 | 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".