Estimating Latent Cycling Trips in Montreal, Canada
Bibliographic record
Abstract
About 1.8% of the residents of the Island of Montreal, in Quebec, Canada, ride a bicycle at least once a day during the fall season. These people make nearly 76,990 daily trips, 2.0% of the total number of commuter trips. Of these cyclists, 65% are men, although men represent only 48.1% of residents, and nearly 63.8% are employed, although the employed represent only 45.2% of the island's population. Data from a large-scale travel survey were used to confirm the influence of various factors on bike use. The study showed that men were 1.99 times more likely to make a trip by bike than were women, individuals who lived in a nonmotorized household were 2.35 times more likely to take a trip by bike than were those who lived in motorized households, people who commuted on a sunny day were 1.46 times more likely to travel by bike, and people who lived more than 9.3 mi (15 km) from the downtown area were 0.29 times less likely to travel by bike compared with people who lived nearer. The study also proposed a methodology for estimating latent bicycle trips, that is, the number of car trips that could be made by bike. When a criterion of travel range based on age cohorts and genders was applied, it appeared that about 50.7% of car trips would be made by bike. When more restrictive criteria were used (consideration of trip chains, shopping), it appeared that 18.2% of car trips would be made by bike.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".