Understanding urban climate-resilient cyclists: A solution to reducing individual motorized transport
Bibliographic record
Abstract
Climate change has increasingly adverse effects on cycling, especially in relation to climate-sensitive hazards such as heatwaves, natural disasters, and air pollution [1]. Modal shifting from cars to bikes is an evidence-based strategy for reducing local greenhouse gas emissions and air pollution in cities [2]. The development of connected and secure cycling infrastructure and improved accessibility to cycle-sharing programs have contributed to an increase in daily cycling trips. Bike supportive policies have further boosted cycling in the US, China, Europe, South America, and Asia [3]. For instance, Amsterdam saw a 15% increase in cycling following infrastructure improvements, while Sevilla and Bogotá had 10% gains, and Vancouver and Paris saw smaller (~5%) increases [3]. Despite the progress, climate change and climate-sensitive hazards increasingly threaten bike use in cities [1]. Here, we summarize current evidence on the effects of heatwaves, natural disasters, and air pollution on urban cycling, highlighting the critical threat to its use. Investigating conditions facilitating the climate resilience of cyclists, defined as their ability to bounce back from climate-sensitive hazards, is essential [4].
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 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".