Cycling data and indicators: a critical ingredient in assigning priority for cycling
Bibliographic record
Abstract
This paper outlines the approach that VicRoads is taking to bring together cycling related data to provide a more comprehensive understanding of the types of trips that cyclists make and the routes that they use; suggests how this information could be used to develop a more sophisticated approach to planning, prioritising and implementing bicycle networks in Melbourne, and demonstrates how this approach has been used to consider and provide an appropriate level of priority for cycling along Whitehorse Road in Melbourne's eastern suburbs as part of the Tram 109 project. A preliminary analysis of the available data has shown that there is a higher proportion of cycling trips in inner city areas of Melbourne, that most cycling trips to work are less than 10 kilometres in length and that the provision of bicycle facilities is likely to be an important factor in increasing the numbers of people who ride bicycles. (a) For the covering entry of this conference, please see ITRD abstract no. E212956.
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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 | 0.106 | 0.197 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.015 | 0.014 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.011 | 0.012 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.008 |
| 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".