Use of SLAMM in Evaluating Best Management Practices
Bibliographic record
Abstract
Once baseline water quality data reveals that beneficial uses of a stream are no longer supported, the task of evaluating alternatives for urban watershed management can be challenging for municipal planners.While working with the municipalities within the Bear Creek watershed to develop a watershed management plan, the Clinton River Watershed Council selected the Source Loading and Management Model (SLAMM) as the main instrument.A costeffective management tool was required to assist in evaluating the effectiveness of urban best management practices (BMPs).Evaluating the suitability of the model was difficult because we were not aware of any previous SLAMM applications in the State of Michigan.The objective became, not only to learn about and apply the model, but also to demonstrate its applicability in similar Michigan watersheds.The Source Loading and Management Model (Pitt, 1998; Pitt and Voorhees 1995) emphasizes the use ofvariable quality of runoff, small storm hydrology, and pmiiculate washoffto calculate runoff pollutant yield estimates.Unlike drainage design models, SLAMM accurately computes nmoff pollutant loads and flows associated with small storm events.This is critical because most ofthe pollutant load is associated with the smaller, frequent runoff events.SLAMM evaluates several conh•ol practices including detention ponds, infiltration devices, porous pavements, grass swales, catchbasin cleaning, and street cleaning.These controls can be evaluated in combinations at many source areas and at the outfalls.Furthermore, SLAMM computes the relative -----------
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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 | 0.028 | 0.078 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".