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Record W2538440968 · doi:10.14796/jwmm.r207-04

A Methodology to Design and/or Assess Baffles for Floatables Control

2001· article· en· W2538440968 on OpenAlexvenueno aff
Thomas Newman

Bibliographic record

VenueJournal of Water Management Modeling · 2001
Typearticle
Languageen
FieldEngineering
TopicHydraulic flow and structures
Canadian institutionsnot available
Fundersnot available
KeywordsBaffleCombined sewerComputer scienceControl (management)Environmental scienceEngineeringCivil engineeringMarine engineeringMechanical engineeringArtificial intelligence

Abstract

fetched live from OpenAlex

Tbis chapter describes an analytical framework for the design and/or analysis of baffles to reduce floatables discharges from combined-sewer overflows (CSOs).This simple analytical framework, which is supported with a spreadsheet model, is compared to its predecessors and its advantages illustrated.These include ease of use, improved applicability to typical installation configurations, and refmed analyses of floatables-removalmechanisms.Refined analyses include a simple accounting for flow path through a chamberincluding a provision for situations where the invert of the inlet conduit is at a higher elevation than the bottom of the baffle -and a simple accounting for floatables captme via the underflow (dry-weather connection) during overflow conditions.Results of the model are compared to the results of the previous approaches and to available laboratory test data for four test cases.Examples of the model application to cases in the City ofNew York are available from the author.Finally, possible areas for future improvement in the model approach are identified.

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)0.002
metaresearch head score (Gemma)0.004
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: Methods · Consensus signal: Methods
Teacher disagreement score0.007
Threshold uncertainty score0.024

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0070.001

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 head0.097
GPT teacher head0.296
Teacher spread0.199 · 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
GenreMethods

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

Citations7
Published2001
Admission routes1
Has abstractyes

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