Bibliographic record
Abstract
Cycle Zone Analysis is a new metric that can help planners and decision makers assess existing conditions and future potential for bicycling and develop strategies to maximize returns on financial investment through increases in mode share. This Geographic Information System (GIS) based analysis considers many factors known to influence bicycling activity, including: land use mix, roadway density and connectivity, bike way quantity and quality, topography, employment and population density. This analysis is similar to the traditional Transportation Analysis Zone (TAZ) based systems used for motor vehicle modeling currently performed at the local, regional and state levels. A second new analysis metric, the Bikeway Quality Index (BQI) is used to measure the quality of on-street bicycle facilities. The methodology allows evaluation of features unique to various types of bike ways (e.g. bike lane width and traffic calming along shared streets). Portland, OR planners used this tool to: better understand the impact of several individual factors on cycling potential in various parts of the city, create a composite picture of existing cycling quality, evaluate future potential for cycling and verify that increased cycling appears to roughly match quality ratings in each cycle zone. This tool was originally developed in a partnership between the Portland Bureau of Transportation and Alta Planning and Design for use in the update of Portland’s Platinum Bicycle Master Plan. Further applications of this tool in areas such as Greater Vancouver British Columbia will allow enhanced calibration and validation of this model as a predictor of cycling potential.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".