Bibliographic record
Abstract
Objective To determine whether buffets have become more common over the last 30 years in Greater Toronto. Methods We measured advertising for buffets (newspapers and Yellow Pages) as a proxy measure of buffet restaurants. Searches were done in two time periods: 1963–1988, and 2011 (up to September). Results Between 1963 and 1988, we found 6 advertisements for buffet restaurants in Toronto (4 in the Yellow Pages and 2 in newspapers). In 2011 we found 16 advertisements (all in the Yellow Pages). Comment Buffets are restaurants that allow unlimited amounts of varied food to be eaten at a fixed price. They combine together several factors that encourage an excessive intake of food energy: they are a fast‐food restaurant, customers can take large portions, and (in most cases) they supply unlimited amounts of energy‐dense food at a relatively low price. Virtually no research has been conducted on the relationship between buffets, energy intake, and weight gain. Our findings indicate that there has been a large growth in the number of restaurants in Toronto offering buffets during the time period that obesity rates were climbing rapidly. These findings are consistent with the possibility that buffets are a factor involved in the epidemic of obesity. Future studies should measure actual food intake when customers eat at a buffet.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".