The much anticipated marriage of cycling and transit: But how will it work?
Bibliographic record
Abstract
help in decreasing congestion, but others debate this claim (2).Up until this point, there has not been any study in the North American context that uses empirical data to measure the actual needs for such integration among cyclists and transit users.Past literature has identified general measures that can facilitate bicycle and transit integration (3); however, details on how this union can work have been in short supply.Recognizing the need for elementary information on the subject for researchers and practitioners alike, this paper seeks to answer two basic questions related to bicycle-transit integration: (a) who are the potential users of this type of intermodal transport?, and (b) what are their current needs and priorities?This research draws on a detailed online survey conducted in Montreal, Quebec, Canada, specifically for this purpose.The survey included demographic, travel behavior, and spatial questions to explore the factors affecting the use of and opportunities for C-T integration.In addition, given the presence in Montreal of Bixi (bicycle taxi), North America's first large-scale public bicycle sharing system, a set of questions was included to measure the potential of this new system to augment the existing public transit service.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.025 | 0.004 |
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".