Bibliographic record
Abstract
In recent years, walking and cycling to school have decreased conversely to the use of private cars due to its greater level of comfort and safety.However, the use of active modes of transport for short journeys is more economically and socially adequate as well as a healthier way of traveling.Active modes of transport can be defi ned as means of travel and transportation at low speed, which take up little space on the road, are air and noise non-pollutant, thus being regarded as more sustainable and a real alternative to cars.On the other hand, urban sustainable mobility implies the defi nition of policies and actions that rely on the promotion of less pollutant, economic, and more equitable modes of transport.To promote this modal shift, some policies focus on the facilities and urban form improvements to increase safety, namely for pedestrians.In this paper a different way to promote the use of active modes in school journeys is proposed and presented, which essentially uses and integrates information from noise and air pollution in the route-planning process to generate healthy routes.The concept of generation of healthy routes was triggered by the need to reduce the exposure to noise and air pollution in school journeys, which can affect the children's health and quality of life.The healthy route model involves the contamination of the distances of the transport network, according to the urban environmental noise levels and the concentration of particles -PM10, thus allowing the defi nition of the less polluted, less noisy and healthier route for active modes.The performance of the model is assessed by calculating the noise and air pollution exposure rates in the obtained routes, in comparison with the shortest route.To validate the model and its potential for the promotion of active modes, a case study is presented in a city center located in North of Portugal for three different school journeys.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".