Bike Share: A discussion and case study analysis Including recommendations for Cal Poly and the City of San Luis Obispo
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Since 2015, micromobility has swiftly expanded to new cities across the United States. Micromobility is defined as a category of transportation services that are shared-use, lightweight, and personal use such as electric scooters (escooters), shared bicycles, and electric bicycles (e-bikes). Micromobility vehicles can be person powered, electrically powered, or a combination of the two (CRCOG, 2022; BTS, 2022). One form of micromobility that is gaining popularity is known as bicycle share. In 2020, the North American Bikeshare and Scootershare Association (NABSA) 2020 State of the Industry Report found that an estimated 83.4 million trips were taken in North America alone (Urbanism Next, 2020). Bicycle share is a type of short term vehicle rental service used in cities across the world. The service typically allows users to rent bicycles through a mobile phone app or a kiosk. Users can ride bikes throughout a bike share system's operating area, which is often contained to select, defined locations such as a city’s limits. There are two major types of bike share in the world. The first is docked, which requires docking stations to charge and store the bikes. In this system, a user can pick up a bike at any station and ride and drop it off at any other empty dock station within the system’s network. The second is dockless, which does not require a docking station, and can be parked anywhere. Recently, it has become standard and more affordable for bike share programs to use both shared bikes and scooters as a hybrid or mixed fleet.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it