Bibliographic record
Abstract
Obesity is becoming an increasing cause of concern worldwide. This thesis examines the determinants and prevalence of obesity, and evaluates the potential health-related cost savings associated with the implementation and promotion of the health claim on low-calorie diets and obesity in Canada. Using data from 2004 Canadian community health survey and reviews of medical/nutritional literature, a Multilevel Multinomial Logistic Regression Model and a variation of Cost of illness approach reveal the following. We found that almost two-thirds of Canadians are overweight/obese. We also found that the aged, males, married, people born in America, less educated, physically inactive, and inadequate fruits/vegetable consumers have increased risk of becoming obese. A 5%-10% reduction in caloric intake due to health-information/health-claims results in nontrivial health-related base savings of CAD$2.09 billion with range of CAD$360 million to CAD$4.18 billion. Stronger economic policies such as subsidies/taxes on low-calorie/high-calorie diets could potentially lead to social optimal calorie consumption.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".