Continuous Distributed Modeling for Evaluation of Stormwater Quality Impacts from Urban Development
Bibliographic record
Abstract
Understanding the impact of land use changes on nutrient and sediment loading from stormwater runoff to a water supply reservoir is the motivation for the study referenced in this chapter.Changes in the loading rate and the relative proportion of nutrients, e.g.nitrogen and phosphorus, can have important effects on eutrophication and algae production in the receiving water of the lake.Evaluation of stormwater runoff quantity and quality is performed for the 30.8 km 2 (11.9 mi 2 ) Rock Creek watershed located within the corporate limits of the City of Norman, OK.This watershed is part of the larger drainage area of Lake Thunderbird reservoir, which is operated by the Central Oklahoma Master Conservancy District and supplies drinking water to Norman and two other surrounding communities.The reservoir was constructed by the US Bureau of Reclamation in 1961-1965.The 2001 bathymetric survey determined Lake Thunderbird to have a maximum depth of 58 ft (17.7 m), mean depth of 15.4 ft (4.7 m), surface area of 5,439 acres (2,211 ha) and volume of 105,838 acre-feet (130,180,000 m 3 ).Excessive algae production leads to taste and odor complaints about the finished water product.Continuous simulation, using the physics-based distributed hydrologic model Vflo ™ , is used to identify runoff and loading rates for three development scenarios.Vflo ™ is a commercial model that has been available
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".