Planning, Engagement and Construction of Blue-Green Systems: Creating Connected and multi-beneficial solutions for the Road Right-of-Way in Vancouver
Bibliographic record
Abstract
In 2019, Vancouver City Council adopted the Rain City Strategy, committing to capture and clean 90% of rainfall that falls within the City, and in doing so, transforming urban watersheds for present and future generations. At the same time, Vancouver is continuing to densify. With increasing density, there is a growing demand for space in the road right-of-way –rainwater management, more and larger servicing utilities, vehicles, transit, bikeways, and wider sidewalks are all competing for spaces. Blue-Green Systems (BGS) are an emerging green infrastructure typology that help the City to meet multiple objectives in the same space. The City and the Park Board are taking steps to actively implement BGSs in the urban area. Showcase projects are bring piloted, such as St. George Rainway, Richard Street BGS and Sunset Park BGS. These pilots are intended to provide benefits on drainage system performance, water quality treatment, combined sewer overflow, climate resilience, biodiversity and equity outcomes, and enhance walking, cycling and recreation opportunities. This presentation will showcase the BGS planning and implementation in Vancouver, explaining the planning process and methodology followed, discussing how the BGSs are optimized to maximize co-benefits, and introducing some of the key trade-offs, such as reduced parking and vehicle access.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.002 |
| Scholarly communication | 0.007 | 0.001 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".