Cycling in Winter: Exploring innovative design principles and practices to support all season bicycle commuting for Winnipeg and Winter Cities worldwide
Bibliographic record
Abstract
Cycling has experienced a renaissance in cities across North America over the last decade as it is embraced by urbanites for its convenience and affordability, and by governments to mitigate road congestion and climate change. Cycling levels are at their peak during the spring and summer season, yet as more people take up cycling they are adapting to winter weather in order to commute year round. Supporting cycling in winter is an underdeveloped area of municipal policy and the focus of this supervised research project. Cities in Canada and the northern United States have winter climates with snowfall, ice, and freezing temperatures for significant periods of the year. Cold weather climates present a variety of challenges to city planners in order to maintain sustainable transportation systems. Winter planning for cycling has become the next “hot topic” in Canada and the United States yet many winter cities are just beginning to look at strategies to promote four season cycling. How can cities determine the best policies, plans and programs to better support cycling in winter? What bicycle route, road network or bicycle facility should a city select to accommodate the requirements of snow removal and winter maintenance? How can these decisions be integrated into long term comprehensive bicycle plans? This project will answer these questions through a literature review, a peer city review and the elaboration of design principles for winter bicycle networks. (...)
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".