Rapid Assessment and Long-Term Monitoring of Green Stormwater Infrastructure with Citizen Scientists
Bibliographic record
Abstract
Green stormwater infrastructure (GSI) has emerged as a promising decentralized management approach to urban stormwater challenges. A lack of data about GSI performance interferes with widespread adoption of GSI. A citizen science program that benefits researchers, lay scientists, and municipalities offers a way to provide these lacking data. We have developed an open-source, transferable green infrastructure rapid assessment (GIRA) protocol for studying the performance of GSI with citizen scientists. This protocol has been tested in six North American cities (New York City, Toronto, Vancouver, Chicago, San Francisco, and Buffalo). In this research we define the performance of GSI in varying geographic, climatic, and maintenance conditions with the intent to create technological, institutional, and management solutions to urban stormwater problems. The GIRA protocol was used by citizen scientists to assess the physical properties and capabilities of bioswales, while small, affordable Green Infrastructure Sensors Boxes (GIBoxes) were used to determine longer-term function across several rain events. Our results indicate that teams of citizen scientists can be effective for collecting and archiving widespread information on the post-installation function of GSI. The effort also showed that citizen scientists had changes in understanding of urban stormwater challenges and the role that GSI can play in solving these problems. We explore the multiple benefits to knowledge, participants, and municipal partners as a result of this research.
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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 | 0.021 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.004 |
| 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".