Bibliographic record
Abstract
Diabetes Mellitus Type 2 is currently a major health issue in both Japan and Canada. When diabetes is not properly managed, it is known to cause diabetes related neuropathy, blindness, and renal disease. Treatment of diabetes and related diseases is a huge financial burden in both countries. Obesity is a leading cause of diabetes. In order for diabetes prevention to occur, obesity must be prevented in the first place. Currently in Japan, approximately10% of children (1.2 million) are mildly obese and 5% of children are moderately or severely obese (600,000). The need for obesity prevention is a pressing matter. This report will examine the causes, various problems, and countermeasures in relation to childhood obesity in Japan, in comparison to The Kahnawake Schools Diabetes Prevention Project (KSDPP), a Canadian case study. Lifestyle-related causes of obesity in Japanese children include (1) over-eating, (2) high-fat diet, (3) lack of exercise, (4) irregular lifestyle, (5) stress. In order to promote health in children, the government, community, school system, medical and social services need to collaborate in making a cohesive program which a) nurtures proper daily habits, b) screens for obesity, c) establishes a health education system within schools, d) provides guidance and action in promoting nutritional balance focusing through school meals, e) encourages physical activity, especially walking, f) teaches the values of the family life, the enjoyment of nature by providing a wholesome and sound environment for children.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".