Low- and no-calorie sweetener intakes from beverages – an up-to-date assessment in four regions: Brazil, Canada, Mexico and the United States
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The current assessment estimated exposure to four low- and no-calorie sweeteners (LNCS) (aspartame, acesulfame potassium (AceK), steviol glycosides and sucralose) from beverages in Brazil, Canada, Mexico and the United States, using up-to-date nationally representative consumption data and industry reported-use level information. Two modelling scenarios were applied - the probabilistic model was guided by reported use level data, with estimated intake for an individual leveraging market-weighted average use level of a particular LNCS in any given LNCS-sweetened beverage type, while the distributional (brand-loyal) model assumed consumer behaviour-led patterns, namely that an individual will be brand loyal to a pre-determined beverage type. Consumer-only and general population intake estimates were derived for the overall population and individual age categories, and compared to the respective acceptable daily intake (ADI) as established by the Joint FAO/WHO Expert Committee on Food Additives (JECFA) for each LNCS. The mean, 90th percentile and 95th percentile intake estimates were substantially lower than the ADI in both modelling scenarios, regardless of the population group or market. In the probabilistic model, the highest consumer-only intake was observed for AceK in Brazilian adolescents (95th percentile, 12.4% of the ADI), while the highest 95th percentile intakes in the distributional model were observed for sucralose in Canadian adults at 20.9% of the ADI. This study provides the latest insights into current intakes of LNCS from water-based non-alcoholic LNCS-sweetened beverages in these regions, aligning well with those published elsewhere.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 it