Building with Nature: Blue-Green Systems for Solving Urban Growth and Climate Challenges in Canada
Bibliographic record
Abstract
Urban development has significantly disrupted the City of Vancouver’s (the City) natural water cycle. Building on multi-year strategic planning, the City is undertaking a major shift in the way infrastructure services are planned, designed and delivered by developing interconnected blue-green systems (BGSs) networks. BGSs, park like networks and corridors, aim to manage water and improve water quality; promote connectivity, active transportation, and recreational; and increase access to nature and biodiversity. The “blue” in blue-green systems refers to integrated water management and green rainwater infrastructure (GRI) services. This function includes nature-based constructed practices like rain gardens, wetlands or other forms of GRI, as well as climate adaptation and flood management functions associated with both minor and/or major rainfall events. The “green” in blue-green system refers to the value of and the services provided by elements of terrestrial vegetation and biodiversity including trees or urban forest as well as other layers of plants, soils and biota present within the system. Together, BGSs support both place-making and functional infrastructure that encourages walking and cycling transportation modes. 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, Alberta Street BGS and Columbia Park Renewal. They 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 these BGS examples in Vancouver, explain the process and methodology followed, discuss how the BGSs are optimized to maximize co-benefits, and introduce some of the key trade-offs, such as reduced parking and vehicle access, that were resolved through community engagement.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.019 | 0.004 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.015 | 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".