The Era of Smartphones: Back to Our Biological Makeup?
Bibliographic record
Abstract
Physical inactivity is a major modifiable cardiovascular risk factor that has become a growing health problem in the 21st century: 83% of adolescents aged 13-15 years and approximately 1/3 of adults worldwide are inactive, that is, not meeting the minimum international physical activity (PA) recommendations (=150 minutes/week of moderate to vigorous PA) [1, 2]. Thus, the PA levels of the general population, especially of individuals at cardiovascular risk, should be routinely assessed by health care professionals, as it has been recently recommended by the American Heart Association [3]. To this end, accelerometers (usually attached to an elastic belt around the waist) allow objective quantification of PA by providing continuous recordings. At least 3 to 5 days of accelerometer monitoring (including weekend days) are required to determine habitual PA, and it is generally accepted that the device should be worn for =10 hours/day [4]. For this reason, the simple and inexpensive method of PA questionnaires is more widely used and generally better accepted. Unfortunately, the validity of self-reported PA is questionable...
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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.015 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.032 | 0.012 |
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".