An Analytical Stochastic Approach for Evaluating the Performance of Combined Sewer Overflow Tanks
Bibliographic record
Abstract
Abstract Storm water detention tanks are widely used for the control of combined sewer overflows. Conventional continuous simulation and recently developed analytical probabilistic models have been used for analyzing the hydrologic operation of storm water detention tanks. These analyses are necessary in order to accurately estimate the runoff capture efficiency of a given control system or the required storage capacity for achieving a desired runoff capture efficiency. The analytical probabilistic models still have the shortcomings of making simplifying assumptions about the initial storage conditions of a detention tank. Developed in this study is a new stochastic analysis method which can provide similar results as provided by continuous simulations and overcome some of the shortcomings of the previously developed analytical probabilistic models. This stochastic analysis method uses closed‐form analytical equations to estimate the runoff capture efficiency and required storage capacity. Results from these analytical equations are validated by comparing with continuous simulation results and close agreements are observed. These analytical equations are therefore proposed as a computationally efficient alternative for analyzing the hydrologic performance of combined sewer overflow tanks.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".