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Record W2320041348 · doi:10.14796/jwmm.r220-34

Sewer Assessment,I/I Assessment and Recalibration Saves Millions

2004· article· en· W2320041348 on OpenAlexvenueno aff
Susan Moisio, Saa K. Shemsu, Philip P. Gray

Bibliographic record

VenueJournal of Water Management Modeling · 2004
Typearticle
Languageen
FieldEngineering
TopicUnderground infrastructure and sustainability
Canadian institutionsnot available
Fundersnot available
KeywordsCombined sewerSanitary sewerMetropolitan areaEnvironmental scienceWater resource managementBasementSewerageEnvironmental engineeringCivil engineeringGeographyEngineeringStormwaterArchaeology

Abstract

fetched live from OpenAlex

The Metropolitan Sewer District of Greater Cincinnati (MSDGC, the District) is committed to the elimination of sanitary sewer overflows (SSO) and basement flooding events in their combined and sanitary sewer networks. In meeting this commitment in the Richmond/Orchard study area, the District has completed a Storm Water Removal Program, conducted flow monitoring and hydraulic modeling, and prepared a remedial measures plan to reduce SSO activity and basement flooding complaints. The recommended remedial plan was considered costly. As a result the District undertook to further evaluate area hydraulic and flooding problems with a focus to improve upon the previous studies when it was determined they did not have the benefit of complete and accurate calibration and system geometry data. A hydraulic model, MIKE SWMM, was developed and calibrated using flow monitoring data collected during the study period and verified using historical monitoring data. Model verification included a more complete consideration of flow, volume, stage, and hydraulic conveyance properties throughout the collection system. The study resulted in a combination of system improvements at an estimated cost of approximately one third of the original estimates to reduce SSO and basement flooding occurrences.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.539
Threshold uncertainty score0.373

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 communication0.0000.001
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 head0.243
Teacher spread0.234 · 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 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

Citations0
Published2004
Admission routes1
Has abstractyes

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