Nonparametric Statistical Tests Comparing First Flush and Composite Samples from the National Stormwater Quality Database
Bibliographic record
Abstract
The University of Alabama and the Center of Watershed Protection, as part of an EPA 1 04(b )3 project, has collected and reviewed phase I NPDES (National Pollutant Discharge Elimination System) MS4 (Municipal Separate Storm Sewer System) stormwater data.The database contains more than 3700 event data sets from 66 municipalities in 17 States.In 1990, communities larger than 100,000 were required to monitor and control the pollutants that reach surface waters from stormwater runoff.Some communities collected grab samples during the first 30 minutes of the event to evaluate the "first flush" effect in contrast to the flow weighted composite samples.There were 417 paired samples representing both first flush and composite samples from eight communities, mostly located in the southeast USA Box and probability plots were prepared for 22 constituents including TSS, TDS, BOD 5 , COD among others.Nonparametric statistical analyses were used to measure differences between sample sets.This chapter shows the results of this preliminary analysis, including the effects of storm size and changes in land use.First flush effect was not present in all the land uses, and certainty not for all constituents.More detailed analyses will be performed as additional data are received.
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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 | 0.015 | 0.056 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".