Continuous Long Term Simulations for Evaluating Storage Treatment Design Options of Stormwater Filters
Bibliographic record
Abstract
Stormwater media filters are used to treat a variety of pollutants at different source areas.These can range from being simple rain gardens or biofilters containing soils or special media, to proprietary devices.Historically, sand filters and sand-peat filters were some of the earliest filters used for stormwater control.Austin (1988), Galli (1990), Shaver (1994), Claytor and Schuler (1996), and Urbonas (1999) all include descriptions and performance information for these fundamental stormwater filtration systems.These filters have been used to treat a variety of conventional stormwater pollutants, mostly focusing on suspended solids and nutrients.Continued research has examined additional media and expanded our understanding of stormwater media filters.Clark and Pitt (1999) include an extensive review of different media, designs, and expected performance.Many proprietary stormwater filters are also now available and usually include cartridges of specialized media that can target specific classes of stormwater contaminants.Descriptions of many of these devices have been described at technical conferences, especially the annual StormCon conference (http://www.stormcon.com/)where vendors have extensive exhibits showcasing these filters.The International BMP Database has much data describing actual field performance for a wide range of stormwater filters (http://www.bmpdatabase.org/).This chapter focuses on an important issue pertaining mostly to the proprietary filters that are sized to be within the guidelines of regulatory agencies.There is much confusion associated with sizing filter installations
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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 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".