A framework for urban storm water modeling and control analysis with analytical models
Bibliographic record
Abstract
This paper introduces a framework for urban storm water modeling and control analysis with analytical models, which consist of a number of functional components, such as rainfall‐runoff transformation, pollutant buildup and wash‐off, and pollutant removal in storage/treatment facilities, etc. As the key component, the rainfall‐runoff transformation, which characterizes runoff generation mechanism in the drainage area, is employed not only to transform the probability distributions of the rainfall characteristics into the probability distribution of runoff in an effort to develop storm water quantity control measures, such as runoff volume of spills and runoff control efficiency, but also to derive the probability distributions of pollutant loads and develop storm water quality control measures, such as the average event mean concentrations and annual pollutant loads to receiving waters. While each component or module is initially developed separately and can function independently, all these components are virtually an integral part of the framework considering that the pollutant wash‐off load model is essentially an integration of the rainfall‐runoff transformation and pollutant buildup models. In light of the proposed framework, which may serve as a map to this study, this paper summarizes theoretical basis while emphasizing major procedures with a case study for the development of the integrated analytical model based on the derived probability distribution approach.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".