Calorie Intake from Alcohol in Canada: Why New Labelling Requirements are Necessary
Bibliographic record
Abstract
We estimated calorie intake from alcohol in Canada, overall and by gender, age, and province, and provide evidence to advocate for mandatory alcohol labelling requirements. Annual per capita (aged 15+) alcohol sales data in litres of pure ethanol by beverage type were taken from Statistics Canada's CANSIM database and converted into calories. The apportionment of consumption by gender, age, and province was based on data from the Canadian Tobacco, Alcohol and Drug Survey. Estimated energy requirements (EER) were from Canada's Food Guide. The average drinker consumed 250 calories, or 11.2% of their daily EER in the form of alcohol, with men (13.3%) consuming a higher proportion of their EER from alcohol than women (8.2%). Drinkers consumed more than one-tenth of their EER from alcohol in all but one province. By beverage type, beer contributes 52.7% of all calories derived from alcohol, while wine (20.8%); spirits (19.8%); and ciders, coolers, and other alcohol (6.7%) also contribute substantially. The substantial caloric impact of alcoholic drinks in the Canadian diet suggests that the addition of caloric labelling on these drinks is a necessary step.
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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 | 0.005 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".