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Record W2935844914 · doi:10.14796/jwmm.c463

Field Evaluation of Discretized Model Setups for the Storm Water Management Model

2019· article· en· W2935844914 on OpenAlexvenueno aff
Robson Leo Pachaly, José G. Vasconcelos, Daniel Allasia, Bruna Minetto

Bibliographic record

VenueJournal of Water Management Modeling · 2019
Typearticle
Languageen
FieldEnvironmental Science
TopicHydrology and Watershed Management Studies
Canadian institutionsnot available
FundersUniversidade Federal de Santa MariaCoordenação de Aperfeiçoamento de Pessoal de Nível Superior
KeywordsStorm Water Management ModelStormwater managementStormStormwaterEnvironmental scienceDrainageDiscretizationHydrology (agriculture)Field (mathematics)Hydrological modellingSurface runoffCivil engineeringGeologyEngineeringGeotechnical engineeringMeteorologyGeographyMathematics

Abstract

fetched live from OpenAlex

The Stormwater Management Model (SWMM) is a hydrologic-hydraulic model often used to simulate water flows in urban drainage systems and changes in water quality. The unsteady flow hydraulic solver in SWMM solves mass and momentum conservation equations for the entire conduit length, and mass is conserved at each junction. This link-node approach used by SWMM does not allow for discretization (i.e. intermediate calculation points) between consecutive junctions, which is adequate in gradual filling scenarios with appropriate calibration and suitable selection of routing time steps. However, because there are more rapid filling scenarios that are associated with intense rain events, the link-node solution approach will affect the accuracy of the hydraulic calculations. This work presents the results of a field investigation in which predetermined volumes of water were suddenly released into a physical stormwater collection system. Level loggers were installed to measure flow depth and outflow rates in these tests.

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)0.003
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: Empirical
Teacher disagreement score0.472
Threshold uncertainty score0.422

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
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 science0.0010.001
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 head0.030
GPT teacher head0.263
Teacher spread0.233 · 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

Citations10
Published2019
Admission routes1
Has abstractyes

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