Potential Data Analysis Methodology to Evaluate the Performance of Manufactured BMPs
Bibliographic record
Abstract
Evaluating the performance of manufactured BMPs on a consistent and scientifically sound approach is beneficial for both the service provider and the services recipient. To do this properly, it is important that these devices need to be tested under a standard set of protocols. The testing data must be collected, reported, and validated prior data analysis. The testing, data collection, data reporting and validation will be addressed under a separate ASCE/EWRI subcommittee. The focus of this paper is to address the data analysis and performance evaluation of manufactured BMPs. To address this issue the existing statistical data analysis methods and performance evaluation that potentially could be used for manufactured BMPs have been examined in this paper. Special attention was devoted to the data distribution and the issue of normality since that will influence the selection of suitable data analysis approach. In general, it has been concluded that the stormwater data is log-normally distributed. The existing BMP performance evaluation has also been evaluated and the effluent probability plot has been recommended to determine the performance evolution of manufactured BMPs.
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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 | 0.061 | 0.137 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".