Trends in Loss-Adjusted Availability of Added Sugars and Energy Contribution from Macronutrients and Major Food Groups in Canada and the United States
Bibliographic record
Abstract
A clear understanding of changes in the consumption of sugars and other sugars-containing foods has become essential for dietary recommendations and nutrition policy considerations. This study aimed to estimate the consumption trends of added sugars, energy, macronutrients, and food categories using food supply data. Annual food availability data were obtained from Statistics Canada "Food Available in Canada" database and compared to the equivalent data from Canadian Community Health Survey 2004/2015 and USDA "Food Availability (Per Capita) Data System". consumption of added sugars (%energy) in Canada over the past two decades, largely attributed to reduced intakes of refined sugar and sugars from soft drinks. Added sugars consumption was generally 30% less than that in the US. There was also a consistent decline in total energy intake and %energy from carbohydrates, accompanied by increased %energy derived from fats particularly during the most recent 10 years. The observed trends in added sugars availability are similar to findings from the Canadian Community Health Surveys, demonstrating the potential application of annual loss-adjusted food availability data in monitoring trends in food and macronutrient intakes over time to complement dietary survey data in informing public policy development.
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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 | 0.002 | 0.007 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".