DESIGNING FOR ENVIRONMENTAL AND INFRASTRUCTURE SUSTAINABILITY: ONTARIO CASE STUDIES FOR RETROFITS AND NEW DEVELOPMENTS
Bibliographic record
Abstract
INTRODUCTION The Low Impact Development (LID) approach has been implemented worldwide for managing stormwater quantity and quality within the context of land development, re-development, and retrofits within an existing development site. Since the inception of the concept in the 1990s, the application of LID has covered different land uses, spatial scales, and environmental objectives, leading to an expanded vision for applying and testing the LID approach. Recently, holistic methodologies and frameworks have linked land planning to key ecological landscapes larger than the previous site scale practice. This new emerging paradigm considers the watershed, subwatershed, and neighbourhood, in addition to the site scale, and consequently, recommends a landscape-based LID and broader Green Infrastructure (GI) solutions (Benedict and McMahon, 2002; Tzoulas et al, 2007; NRDC, 2011). As part of the holistic understanding of land planning and environmental features and functions within the intended spatial scale, LID and GI measures have been designed and constructed as retrofit measures (i.e., measures implemented within existing development) and as measures implemented within new development areas. Under this new paradigm, the land planning context is linked to environmental objectives to provide end points for environmental conservation and restoration within an ecological landscape such as watersheds, subwatersheds, and stream corridors. This paper presents three case studies for the design and construction of LID and GI measures within different land use contexts and for providing multiple environmental objectives.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.010 | 0.005 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
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".