What about Free-Floating Carsharing?
Bibliographic record
Abstract
In recent years, carsharing has become a practical, ecological, and economical alternative to private car ownership around the world. Traditional carsharing is station-based, but new types of shared services have appeared recently. One of those is free-floating carsharing. It is more flexible, but what is its impact on user behavior? This paper aims to characterize the use of the free-floating carsharing service in central Montreal, Canada. Use of the traditional, station-based service and the new service was compared. Because some people were members of both services, it was possible to examine the specific contribution of each service to meeting travel needs. The impact of the introduction of this new transportation alternative was also examined. The results showed that compared with traditional carsharing, more women were members of the free-floating service and the trip distances and durations were much shorter. Shopping was the most important activity, and there was a concentration of trip ends near the central business district in the midday period. When asked what mode users would have used in the absence of the free-floating service, people mentioned public transit, taxis, and walking; the popularity of these alternatives varied, probably in relation to seasonal changes. Further studies are required to measure the environmental impact of this new transportation mode.
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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.015 | 0.003 |
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".