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Record W2310762397 · doi:10.14796/jwmm.r220-08

Analysis of SSO Control Alternatives within the City of Detroit's Regional Collection System

2004· article· en· W2310762397 on OpenAlexvenueno aff
Michael Taylor, Philip Brink, Marc C. Stonehouse

Bibliographic record

VenueJournal of Water Management Modeling · 2004
Typearticle
Languageen
FieldEnvironmental Science
TopicUrban Stormwater Management Solutions
Canadian institutionsnot available
Fundersnot available
KeywordsControl (management)Regional scienceGeographyComputer scienceArtificial intelligence

Abstract

fetched live from OpenAlex

A number of Detroit Water and Sewerage Department's (DWSD) wholesale customers experience effects of sanitary sewer overflows (SSO), including basement flooding.As part of DWSD's wastewater master plan (WWMP) project, the city has investigated various local and regional alternatives for SSO control.SSOs, releases of untreated sewage into the environment, are illegal in Michigan.Related to basement flooding, SSOs have the potential to cause property damage and may present public health concerns.The Michigan Department of Environmental Quality (MDEQ) policy is to identify sanitary sewer overflows, and take appropriate action to eliminate them.Several customers approached DWSD to request additional contract capacity as an approach for eliminating SSOs in their collection system, and in spring of 2001, DWSD and its customers began a collaborative process to examine solutions to eliminate the public health and water quality impacts of SSOs in the regional collection system.The purpose of this study was to find a cost-effective solution for SSO elimination in the service area.The following alternatives were evaluated:• elimination of the sources of high infiltration/inflow (III) that cause SSOs, • local storage or treatment of SSOs, and • regional transmission, storage, and treatment of SSOs.

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)0.003
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.297
Threshold uncertainty score0.591

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.019
GPT teacher head0.219
Teacher spread0.200 · 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 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
Published2004
Admission routes1
Has abstractyes

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