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

2012· article· en· W2127110489 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

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.

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: Empirical
Teacher disagreement score0.357
Threshold uncertainty score0.279

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 head0.017
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