How Many Steps Do you Have in Reserve?
Bibliographic record
Abstract
The aim of this study was to estimate the benefits that people could achieve by trading their car for a nonmotorized mode of travel, such as walking, to make their short daily trips. For this purpose, detailed information on travel behavior gathered through large-scale travel surveys conducted in the greater Montreal, Quebec, Canada, area was used. The travel behavior observed in recent travel surveys was analyzed to estimate the number of short trips for various population segments. These surveys gathered travel and sociodemographic information for approximately 5% of the population. Data from the 2003 survey revealed that more than 7 million motorized trips were made during a typical weekday; 862,000 (11.7%) were shorter than 1.6 km (1 mi). With the appropriate speed and stride for each population segment, these motorized kilometers were converted into numbers of steps to appraise the potential physical activity benefits of making these short trips by foot instead of by a motorized mode. The results show that about 837,000 motorized kilometers could be converted into almost 1,156 million steps every day. Overall, 12.5% of the population had steps in reserve, an average of 2,660 steps per person. Such a shift in mode choice could help some people meet their required physical activity volumes through their daily travel patterns while helping to save energy, reduce pollution, and mitigate traffic congestion.
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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 | 0.000 | 0.003 |
| 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".