Cycle Track Concepts for Burlington, Ontario: Design Lessons from Apeldoorn, Netherlands
Bibliographic record
Abstract
Burlington (ON) has just completed an update to their Cycling Master Plan but with a Dutch twist-their twin city Apeldoorn, the Netherlands, sent one of their bikeway traffic engineers, Mr. Wim Mulder, to assist in defining improvements to get more people cycling. A concept for a for the cycling network was developed. This concept focuses on improving cycling quality of two existing linear trails that traverse the City, creating a new crossing of the QEW freeway, introducing cycle tracks on a future rapid transit corridor along the northern boundary of the City, and filling in the framework with bike lanes and bicycle priority streets. Mr. Mulder's experience and impressions of Burlington are outlined. The design concept of European cycle tracks is introduced. These are intended to address increasing the comfort of cyclists along busy, higher speed roads. The design quality, maintenance standard and legislative environment that are required to make them work in Burlington ON are discussed. The presentation will compare and contrast photos of existing conditions in Burlington with Apeldoorn, and provide North American examples of similar design elements. For the covering asbtract of this conference see iTRD number E217481.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.022 | 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".