RE: "INFLUENCE OF EXERCISE, WALKING, CYCLING, AND OVERALL NONEXERCISE PHYSICAL ACTIVITY ON MORTALITY IN CHINESE WOMEN"
Bibliographic record
Abstract
The reduced risk of mortality reported by Matthews et al. (1) for women with active lifestyles, achieved by participating in either exercise or nonexercise activities, is an intriguing finding. One limitation of the study, acknowledged by the authors, was the use of retrospective self-reports to estimate energy expenditure from exercise and nonexercise activities. While the use of objective measures of energy expenditure, applied prospectively, would have overcome this limitation, it is important to note that the study by Matthews et al. (1) (n = 67,143) simply would not have been feasible using currently available technology. Indeed, it is unlikely that today's objective approaches, such as doubly labeled water or accelerometers combined with heart rate monitors, could realistically be applied in studies with several thousand participants. Hence, we agree with Sesso (2) and with Matthews et al. (3) that self-reported methods are, and will continue to be, the primary approach to quantifying activity in large epidemiologic studies.
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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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.027 | 0.026 |
| Insufficient payload (model declined to judge) | 0.005 | 0.007 |
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".