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Record W2322489266 · doi:10.1061/41009(333)89

Integrated Water Management Demonstration Project for Low Impact Development Urban Retrofit and Decentralized Wastewater Treatment Systems in the Upper Patuxent River Watershed, Prince George's County, Maryland

2008· article· en· W2322489266 on OpenAlexaboutno aff
Alfonso Blanco, William E. Roper, Mow-Soung Cheng

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEnvironmental Science
TopicUrban Stormwater Management Solutions
Canadian institutionsnot available
Fundersnot available
KeywordsLow-impact developmentStormwaterGeorge (robot)Environmental scienceStorm Water Management ModelWatershedWater resourcesStormBioretentionWastewaterWater resource managementEnvironmental planningEnvironmental engineeringStormwater managementGeographyComputer scienceMeteorologySurface runoff

Abstract

fetched live from OpenAlex

Prince George's County and their partners (U.S. EPA, City of Laurel, Prince George's County Public Schools, Maryland National Capital Park and Planning Commission, Lowe's Home Improvement Center, and Patuxent River 4-H Center) have been collaborating on a comprehensive storm water management plan using Low Impact Development (LID) for urban retrofit and decentralized wastewater treatment system. The Demonstration Project was funded by a Congressional Earmark Grant of Total Project amount of $1,324,667 the Federal Grant portion is $993,500 (75%) and match portion is $331,167 (25%). LID is a concept that began in Prince George's County, Maryland in 1990 as an alternative to traditional storm water Best Management Practices (BMP's) installed at construction projects. The LID project components are an integrated storm water management approach using LID techniques to retrofit a mixed use, high density area and a decentralized wastewater treatment system. LID techniques can be simple, but cost effective instead of depending on expensive, and complicated collection, conveyance, storage and treatment systems. The LID techniques used in this project are bioretention cells, grass swales, rain barrels/cisterns, green roofs, Bayscaping, and permeable pavements. LID techniques can also play an important role in Smart Growth, Green Infrastructure, and Land Use Planning. The implementation of these techniques will reduce water consumption, run-off, and non-point sources.

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 score0.337
Threshold uncertainty score0.772

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 head0.018
GPT teacher head0.224
Teacher spread0.206 · 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

Citations2
Published2008
Admission routes1
Has abstractyes

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