Stormwater Databases: NURP, USGS, International BMP Database and NSQD
Bibliographic record
Abstract
During the past 30 years government agencies, universities and private companies have invested millions of dollars trying to understand the factors that affect the quality of stormwater discharges.Stormwater can pick up dirt, chemicals, and other pollutants that are then discharged to the stormwater drainage system and ultimately discharged to receiving water bodies, usually without any treatment.According to the U.S. EPA, stormwater is one of the major causes of water quality impairment in the nation's rivers, lakes, ocean shorelines, and estuaries.In the U.S., several nationwide monitoring efforts have examined the sources and resulting concentrations of stormwater pollutants.Some of the results of these studies have been stored in electronic databases.For example the International Best Management Practices Database (BMP database) uses a database application to store and retrieve information from different stormwater controls.Another example is the National Stormwater Quality Database (NSQD) that stores some of the reported data from the National Pollutant Discharge Elimination System (NPDES) Phase I stormwater monitoring program in a spreadsheet format.Two previous national stormwater monitoring efforts have included the Nationwide Urban Runoff Program (NURP) finished in 1983 and the USGS urban stormwater database completed in 1987.The results from both of these efforts were stored on magnetic tapes with a proprietary format.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.017 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.077 | 0.046 |
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".