Stormwater Quality Descriptions using the Three Parameter Lognormal Distribution
Bibliographic record
Abstract
The cumulative probability distribution used to describe the variability of stormwater pollutant concentrations has been a matter of interest in recent years.Many predictive models attempt to estimate appropriate stormwater constituent concentrations based on land use and the amount of impervious area.The most important study that characterized stormwater was the Nationwide Urban Runoff Program (NURP) (EPA 1983).NURP was conducted throughout the U.S. and included about 2300 events from 1978 through 1982.One of the conclusions of the final NURP report was that the event mean concentrations (EMCs) of stormwater constituents were described by lognormal distributions.This finding has been re-evaluated recently, with the conclusion that not all stormwater constituents are adequately described by lognormal distributions (Van Buren, 1997; Beherra, 2000).Stormwater managers have generally accepted the assumption of lognormality of stormwater constituent concentrations between the 5th and 95th percentiles.Based on this assumption, it is common to use the logtransformed EMC values to evaluate differences between land use categories and other characteristics.Statistical inference methods, like estimation and test of hypothesis, and analysis of variance (ANOVA) require statistical information about the distribution of the EMC to evaluate these differences.The use of log-transformed data usually includes the location and scale parameter, but a lower bound parameter is usually neglected.In this chapter, a large database, the National Stormwater Quality Database v.1.1 (NSQD) (Pitt, et al. 2003), will be used to evaluate a three-parameter lognormal distribution for stormwater constituent concentrations for different land uses.The NSQD
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".