Site suitability and public participation: a study of bike sharing stations in a college town.
Bibliographic record
Abstract
Concerns about global climate change, energy security, and unstable fuel prices have motivated decision makers worldwide to explore sustainable options for transportation (Schäfer 2009). One strategy supported by transportation planners is bike-sharing programs (BSP), which ease both traffic volume and provide sustainable and green options for urban environments (García-Palomares, Gutiérrez, and Latorre 2012). Users of BSPs can take advantage of biking without the responsibilities of bike purchases, maintenance, and obligations related to parking and storage. Moreover, BSPs incorporate cycling into the public transportation system (Shaheen, Guzman, and Zhang 2010), providing transit users an option that offers mobility and flexibility at a lower cost (Metro Vancouver TransLink 2008). In the U.S., there are several implemented examples of BSPs, such as Hubway in Boston, MA; Smartbike in Washington DC; and NiceRide in Minneapolis, MN (US DOT 2012). However, according to DeMaio and Gifford (2004), BSPs are not suitable for all American cities. BSPs are more appropriate for “urban areas with more compact downtowns, university campuses, and dense neighborhood with a concentration of younger people" (DeMaio and Gifford 2004, 11).
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.008 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".