Assessing Physical Activity Achievement by using Transit
Bibliographic record
Abstract
Sedentary lifestyle is an important public health issue. To prevent this problem, major health organizations promote the inclusion of physical activity in daily life. Active modes are therefore a well-known way of achieving the health recommendations but walking to transit has also been studied recently. The goal of this study is to assess the level of physical activity achieved by using transit, to verify its contribution in reaching the recommendations. The paper aims to assess the energy expenditure associated with transit use by analyzing the related Metabolic Equivalent of Task. This allows us to express trips as physical activity expenditures and to integrate them in the daily pool of physical activities. For this study, only the main variables affecting the intensity of physical activity are considered. These are the walking time and slope encountered during the walking portion of transit trips. This estimation allows us to estimate the level of physical activity reached by transit users and assess the potential physical activity drivers could achieve if they switched to transit. Finally, the method is also applied to a current transportation issue in Montreal. Results show that transit users living in the Montreal area can achieve 54% of their recommended daily physical activity just by using transit. Current users of motorized modes, if they were to change to transit for their daily travels, could achieve 85% of the recommended daily physical activity.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".