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Record W2315475544 · doi:10.1061/41099(367)53

LID Education and Installation in Mixed Income and Ethnically Diverse Areas of Milwaukee, Wisconsin

2010· article· en· W2315475544 on OpenAlexaff
Gary J. Belan, Cheryl Nenn

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEnvironmental Science
TopicUrban Stormwater Management Solutions
Canadian institutionsAmerican Water (Canada)
Fundersnot available
KeywordsBioretentionLandscapingStormwaterEnvironmental planningEnvironmental scienceYardBusinessEngineeringCivil engineeringSurface runoffEcology

Abstract

fetched live from OpenAlex

From 2005 to the present, American Rivers and the Milwaukee Riverkeeper have been involved in the installation of a variety of LID practices in the Johnson's Park neighborhood of Milwaukee. During that time, the two organizations worked to educate homeowners and cultivate interest in a grant program to design and install rain gardens, rain barrels, and downspout disconnects to volunteers within the neighborhood. The goal was to reduce stormwater pollution in an urban area and to explore the basic social and economic factors that might affect the installation. Rain barrels were installed at four houses and bioretention cells were installed at seven houses. While the installations themselves were successful at mitigating stormwater challenges arose with homeowner/non-profit communications and expectations. Some participants were unhappy with the natural landscaping of the rain gardens and replaced native plants with turf grass. The modified soil that was applied during the garden construction phase is the primary medium in infiltrating water, and the cells have turned out to be very effective even with turf grass. We concluded that homeowners should be given a choice of designs and rooted plants should be used as opposed to plant seeding. Minimizing the different varieties of plants would also be helpful in more clear identification of weeds during maintenance. Additionally, turf grass should be considered an option as an infiltration cover medium for residential projects.

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)0.000
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.018
Threshold uncertainty score0.701

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.009
GPT teacher head0.226
Teacher spread0.217 · 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 designObservational
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
Published2010
Admission routes1
Has abstractyes

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