How far have roadside curb inlets evolved towards sustainable urban drainage?
Bibliographic record
Abstract
• Short curb inlets direct stormwater to Green Stormwater Infrastructures, enhancing sustainability and reducing flood risks. • Efficient design requires considering inlet shape, slope, flow, and climate resilience. • Sustainable curb inlets improve water quality, reduce heat, and promote eco-friendly stormwater management. • Global implementation relies on governance, awareness, retrofits, and continuous monitoring. Stormwater management has become a critical issue, particularly with the ongoing urbanization and the impacts of climate change. Roadside curb inlets are key components of grey infrastructure that convey stormwater to various drainage systems. Curb inlets for conventional drainage systems are typically long, whereas they are usually shorter for directing stormwater to sustainable green stormwater infrastructures (GSIs), such as a roadside bioretention cell. As shorter curb inlets drain stormwater to GSIs, they have noteworthy advantages over conventional inlets such as environmental sustainability, urban flood resilience, pollution control, improved public health, and mitigating urban heat stress. This perspective aims to present a global outlook on the implementation of sustainable GSI curb inlets while also exploring the transition from conventional to sustainable systems. While some countries such as the USA, Canada, and China have adopted sustainable drainage practices including curb inlets, most regions, such as South Asia, Central America, and Africa are still far from embracing these practices. For the wider implementation of sustainable curb inlets with GSIs, recommendations include framing policies at the ministry level, raising awareness through research institutes, and educating the public on the benefits of sustainable drainage. For efficient design, it’s crucial to understand curb inlet hydraulics, consider various design parameters, monitor for clogging and sediment buildup, and account for climate change impacts.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.006 | 0.010 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.014 | 0.003 |
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".