Probabilistic Models for Analysis of Urban Runoff Control Systems
Bibliographic record
Abstract
Given the significant urban runoff impacts on many receiving waters and the massive costs of future investments in drainage infrastructure, the design of urban runoff control systems must be cost-effective. Cost-effective design requires that various runoff control system alternatives be investigated at the planning stage so that cost-effective runoff control systems can be identified for design level analysis. To analyze the runoff control performance of various combinations of runoff control systems at the planning stage, efficient screening models are acutely needed. For this purpose, analytical probabilistic models were applied to analyze the runoff quantity/quality control performance of various combinations of storage and treatment systems. These analytical probabilistic models are developed with derived probability distribution theory whereby the input meteorology to the catchment is described by probability density functions (PDFs) of the meteorological characteristics that are transformed by hydrologic/hydraulic functions to PDFs of the system performance variables. The resulting PDFs are then used to determine the average performance conditions. These models provide closed-formed solutions of the performance equations that are highly efficient in both a conceptual and computational sense. As a result, they are particularly useful for the screening analysis of urban runoff control alternatives.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".