Techniques to Assess Rain Gardens as Stormwater Best Management Practices
Bibliographic record
Abstract
Rain gardens are an aesthetically pleasing stormwater best management practice (BMP) that reduce runoff volume and remove stormwater pollutants through the processes of infiltration/filtration, adsorption, evapotranspiration, and plant uptake. Monitoring programs are often used to evaluate the performance of stormwater BMPs such as rain gardens. Monitoring a large number of rain gardens, however, is impractical due to the time and cost requirements. It is of interest, therefore, to develop other techniques to determine the effectiveness of rain gardens. The assessment program is aimed to assist municipalities in evaluating the effectiveness of BMPs for purposes of construction due diligence, NPDES permit requirements, and determining maintenance requirements. The primary process through which runoff volume is reduced in rain gardens is infiltration of water through the soil. Thus, infiltration rate is a key assessment parameter for rain gardens. Two methods for determining the infiltration rates of rain gardens have been developed as part of a tiered four level assessment protocol: 1. visual inspection, 2. capacity testing, 3. synthetic runoff testing , and 4. monitoring.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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; both teacher heads agree on what is shown here.
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".