ADIPOSITY AND INTERNALIZING PROBLEMS: INFANCY TO MIDDLE CHILDHOOD
Bibliographic record
Abstract
Book synopsis: Obesity has become the number one health threat to Americans, but the incidence is most tragic for our children and teenagers. Nearly 1 in every 7 boys and girls is obese and far more are overweight. Most developed countries including the United Kingdom and Canada are seeing similar rates. In these volumes, a cross-disciplinary team of experts presents what we know and are learning about the causes of youth obesity, its affects, solutionfs, and future prevention. Contributors focus on the newest research from fields including pediatrics, genetics, nursing, nutritional science, surgery, psychology, advertising, geography, and landscape architecture. Obesity among our young has grown to epidemic proportions and sets our young up for a lifetime of phusical illness including diabetes, heart disease, and cancer, as well as psychological disorders from anxiety to depression and chronic stress. Yet the causes and solutions are not as easy to understand and address as we might think. Topics addressed in these volumes include obesity from infancy across the life span, how the brain is affected by obesity, medical outcomes, medication and obesity, nutrition and the affect of supersized foods, the role of the media and built environments. Social disparities, family obesity, the role of television and video games, effective weight-loss programs, bariatric surgery, and ethical issues are also among chapter topics.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.031 | 0.006 |
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".