Effect of Community Road Infrastructure, Socio-Demographic and Street Pattern in Promoting Walking as Sustainable Transportation Mode
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Background: Traffic demand is growing worldwide and the increased carbon emission from transport and travel activities is contributing to greenhouse gas emission and climate change. As the oil and gas capital of Canada, the city of Calgary has a very high carbon footprint per population and the reduction of automobile use is an important policy goal for the city. Walking, a part of active transportation promotes sustainable transportation initiative by reducing greenhouse gas emission. To encourage walking, favorable walking environment should be ensured which largely depends on street pattern and connectivity. However, the effect of street pattern on walking at community level was not explored much in previous studies, particularly at rapidly expanding city such as Calgary’s context. Aims and Objectives: The study identifies the effects of different neighborhood design and planning factors associated with the share of walking in work trips while controlling for differences in social economic characteristics of the neighborhood. Methods: A linear regression model was developed using community-level data from the 2011 census and the road infrastructure data of Calgary. Results: Our study finds that different street patterns and types of land use, length of train tracks, number of train stations and number of schools have significant effect on walking. Conclusion: Thus, different neighbourhood street patterns and land uses should be considered in the development of new communities for promoting active and sustainable transportation.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it