A Critical Review of Existing Methods to Evaluate the Performance of Nature-Based Solutions (NBS) on Commercial Roofs (CR) to Mitigate Urban Flooding
Bibliographic record
Abstract
The intensity and frequency of extreme rainfall events have increased in North America and the world due to climate change. Extreme rainfall events, characterized by a heavy volume of rainfall in a short duration, have triggered the onset of urban flash floods. Over the years, flash flooding has been reported in different cities in Canada, which resulted in many losses. Subsequently, different green roofing systems have been adopted to control urban stormwater runoff as part of Nature-Based Solutions (NBS) to mitigate urban flood and build a flood-resilient city. Currently, no specific widely recognized standard or code is dedicated to determining the hydrological performance of green roofs as a whole system. Moreover, there are no test protocols to regulate the design of green roof systems in the market. A comprehensive literature review examines existing research methods adopted to evaluate influencing parameters affecting the hydrological performance of NBS-CR. The results indicate several limitations in experimental and field investigations. Consequently, to address these limitations, it is essential to formulate a multi-functional work plan to develop a standardized test method that can become a common platform for the roofing industry to test and quantify the hydrological performance of their systems.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".