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Record W4386613257 · doi:10.1061/jhyeff.heeng-6015

Incorrect Sizing Calculation Methods for Bioretention Cells

2023· article· en· W4386613257 on OpenAlexaffabout
Yiping Guo

Bibliographic record

VenueJournal of Hydrologic Engineering · 2023
Typearticle
Languageen
FieldEnvironmental Science
TopicUrban Stormwater Management Solutions
Canadian institutionsMcMaster University
Fundersnot available
KeywordsBioretentionSizingEnvironmental scienceLow-impact developmentComputer scienceHydrology (agriculture)Stormwater managementSurface runoffStormwaterGeotechnical engineeringGeologyEcologyChemistry

Abstract

fetched live from OpenAlex

Water quality control bioretention cells are usually sized so that they are large enough to provide storage of runoff generated from its catchment resulting from the water quality control design storm of the location of interest. A bioretention cell can have three parts where runoff may be temporarily stored: (1) the depressed surface ponding area, (2) the growing media layer, and (3) the storage layer. In this study, the detailed sizing calculation methods adopted by 21 jurisdictions in the US and Canada were reviewed and compared. It was found that for satisfying the required storage volume to store runoff generated from the water quality control design storm, some jurisdictions only allow the storage provided by the surface ponding area to be counted, whereas other jurisdictions allow the sum of the storages provided by two of the three or all of the three parts to be counted. These differences can result in significant differences in the sizes and performances of bioretention cells. By examining and analyzing the detailed hydrologic processes involved in the operation of bioretention cells, it was demonstrated in this study that many of the adopted sizing calculation methods are flawed or inappropriate. Presented here are a summary of the findings and the recommended sizing calculation method. The possible degrees of over- or underdesigns resulting from some of the incorrect sizing calculation methods are also estimated.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.567
Threshold uncertainty score0.264

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.018
GPT teacher head0.286
Teacher spread0.268 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2023
Admission routes2
Has abstractyes

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