Green rainwater infrastructure in the real world: City of Vancouver performance results
Bibliographic record
Abstract
The City of Vancouver is leading the way in constructing Green Rainwater Infrastructure (GRI) in Vancouver as a means of transforming how we view rainwater. GRI uses a suite of technologies such as bioswales, rainwater tree trenches and infiltration trenches that help mimic the natural hydrological cycle by capturing and treating rainfall runoff close it where it lands and diverting large amounts of water from the sewer system. The primary objective of GRI systems is to retain rainwater runoff from hard surfaces, and return that water to groundwater recharge or evapotranspiration instead. The Rain City Strategy has set an ambitious target of retaining 48 mm of rainfall runoff over a 24-hour period. We monitored the performance of 13 GRI assets at 6 locations, consisting of bioswales, infiltration trenches and rainwater tree trenches. This presentation will show the results of this monitoring program and if the GRI performance objectives are being met. Through monitoring soil moisture, water level and flow, we evaluated the hydrologic performance and the ability of GRI assets to maintain plant health. We present the lessons learned from performance monitoring and how this type of monitoring informs design and future maintenance.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 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) | 0.003 | 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".