Obesity in Childhood and Adolescence [2 volumes]
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
<JATS1:p>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.</JATS1:p> <JATS1:p>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.</JATS1:p>
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it