A week of physical inactivity has similar health costs to smoking a packet of cigarettes
Bibliographic record
Abstract
Google knows everything, so we tried ‘What is the health cost of a week of physical inactivity compared with the health cost of cigarette smoking?’ 915 000 results provided no clear answer. PubMed, on the other hand, was concise. Two results but no help. What is your guess? Does a day of physical inactivity affect your health like just a couple of smokes (no big deal), or like a packet of 20? How much physical inactivity damages health as much as a carton of cigarettes? Given Google's lack of help, we reused some backs of envelopes. It is really not hard. The US population in 1999 was 272 million. The percentage inactive ranged from 29% to 48%, depending on your source. You could use both of these numbers for a sensitivity analysis, but let's be optimistic: 29%=79 million inactive …
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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 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.007 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.043 | 0.005 |
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".