Analyses Of The Hydrologic Performance Of Bioretention Facilities
Bibliographic record
Abstract
Due to urbanization, and replacing natural pervious lands by impermeable surfaces, the patterns of rainfall-runoff are altered and thus, negatively influence natural water systems regarding both water quantity and water quality. Bioretention as an efficient LID practice has received significant interest in the recent years. Bioretention practice due to its advantages can be considered as one of the most promising LID practices that maintains the fundamental hydrologic functions in a natural environment and can be integrated into neighborhood landscaping. The primary objective of the current study is analyzing the effects of inflow and outflow characteristics on right-of-way (roadside) bioretention facilities. Inlet and outlet flow hydrographs under several design storm conditions were examined. After the formulation of a SWMM model (node and link plus LID), numerical experiments including sensitive analysis will be designed to simulate and investigate the runoff control performance of a right-of-way bioretention facility. The effective length of the bioretention was found by FLOW3D software (finite element). The performance of the bioretention cell with the effective lengths (12 &16m) reinvestigated and results compared to original bioretention cell
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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 | 0.000 | 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 | 0.000 | 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) | 0.001 | 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".