Data Reporting Guidelines for Certification of Manufactured Stormwater BMPs: Part II
Bibliographic record
Abstract
Data Reporting guidelines presented here were developed as part of the ASCE/EWRI Task Committee on Guidelines for Certification of Manufactured Stormwater BMPs. This work is the collaboration of the Stormwater Infrastructure Committee of EWRI's Water, Wastewater, and Stormwater Council (WWSC) and the Wet Weather Flow Technology Committee of the Urban Water Resources Research Council (UWRRC). These guidelines were developed by review of the major manufactured treatment device certification protocol requirements drawing primarily from the Technology Assessment Protocol-Ecology (TAPE) and the Technology Assessment Reciprocity Partnership (TARP). These reporting guidelines have been broadened to support the International Stormwater Best Management Practices (BMP) Database. With the increasing need for the field testing of proprietary devices comes the importance of consistent data reporting guidelines to be used when reporting to regulatory agencies or designers. The need for standardized reporting is underscored by the tremendous impact the range of testing factors can have upon testing results. These factors include the testing environment, experimental design, testing methodologies, statistical analysis, and data presentation. The need for consistency is underscored by the complex influence these factors have upon performance results. A clear and consistent data reporting approach can ensure that these biases are minimized, well understood, and that representative field testing can be effectively evaluated by the regulatory agency. A consistent reporting format is also needed to aid vendors to efficiently navigate the complicated application process for device certification. Finally, an independent third-party is needed to either conduct or review the testing to ensure testing impartiality. The committee membership includes stakeholders from the regulatory, academic, manufacturing, and design communities.
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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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 teacher head, 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".