The epidemic has gone global: can Exercise is Medicine help quell the tide?
Bibliographic record
Abstract
The epidemic is physical inactivity and the resultant chronic diseases. Since inactivity is hard to measure, we tend to focus on its sibling, obesity. The physical inactivity epidemic seemed to start in the USA in the 1970s. And, if there's one thing the USA is not shy about, it's about sharing its supersized and sedentary culture (actually, there's not much the USA is shy about.). Now the USA, with two-thirds of its adults overweight or obese, is feeling the hollow pride of a country that set sail, got several islands in the South Pacific to jump on board, but then realised that the trip had ill-intended consequences. Like infectious epidemics, physical inactivity is spreading. Canada, New Zealand, Australia, much of South America and much of Europe have populations where over half of adults have a body mass index (BMI) above 25 (the cut-off for overweight). Globally, there are over 1 billion adults with a BMI above 25. To stress that obesity is a global problem, the WHO has coined the term, ‘globesity’. …
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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 | 0.004 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.004 | 0.010 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.048 | 0.048 |
| Insufficient payload (model declined to judge) | 0.010 | 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".