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Explicit Equation for Estimating Storm-Water Capture Efficiency of Rain Gardens

2012· article· en· W2127110489 on OpenAlexafffund
Shouhong Zhang, Yiping Guo

Bibliographic record

VenueJournal of Hydrologic Engineering · 2012
Typearticle
Languageen
FieldEnvironmental Science
TopicUrban Stormwater Management Solutions
Canadian institutionsMcMaster University
FundersNatural Sciences and Engineering Research Council of CanadaChina Scholarship CouncilU.S. Environmental Protection Agency
KeywordsStormEnvironmental scienceHydrology (agriculture)MeteorologyWater qualityStorm Water Management ModelAtmospheric sciencesStormwaterSurface runoffGeographyGeologyEcology

Abstract

fetched live from OpenAlex

Rain gardens have increasingly been used to control the adverse effects of urbanization on storm-water quantity and quality. The ratio or percentage of storm water generated from the contributing area of a rain garden that is captured by the surface depression of the rain garden is known as its storm-water capture efficiency. This capture efficiency is an important indicator of a rain garden’s performance for storm-water management. Based on the probability distributions of local rainfall event characteristics and the hydrologic operation of rain gardens, an explicit analytical equation is derived for estimating the long-term average storm-water capture efficiency of a rain garden. The validity of the analytical equation is demonstrated by comparing its outcomes with results from a series of continuous simulations. Example applications of this equation are made for two locations.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.022
Threshold uncertainty score0.044

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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.

Opus teacher head0.017
GPT teacher head0.213
Teacher spread0.196 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreReview

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

Citations56
Published2012
Admission routes2
Has abstractyes

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