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Record W4255045654 · doi:10.32920/ryerson.14663337.v1

Dynamic analysis of vertical stormwater storage systems

2021· preprint· en· W4255045654 on OpenAlexaff
Kay Taegyeong Kang

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEnvironmental Science
TopicUrban Stormwater Management Solutions
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsInflowStormwaterDrop (telecommunication)Water hammerEnvironmental scienceGeotechnical engineeringFlow (mathematics)GeologyMechanicsPetroleum engineeringEngineeringSurface runoffMechanical engineering

Abstract

fetched live from OpenAlex

A below-grade vertical stormwater storage system is one of the solutions to reduce the volume of sewer overflows released into the environment. The system is submerged most of the time during filling, which can result in hydraulic problems. This research intent to provide some insight on potential hydraulic problems that can occur in a vertical storage system during intense rain events. An experimental study was conducted using a physical scale model that consists of two vertical storage shafts, a horizontal tunnel and an inflow drop shaft. The results showed that both entrapped air in the system and mass flow oscillation in the system can cause a rapid rise of water level, or a geyser, at the drop shaft. The predictions of a modified version of HAMMER compared well with the experimental result while the InfoWorks CS model was unable to simulate vertical momentum in the drop shaft.

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)0.000
metaresearch head score (Gemma)0.000
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 score0.018
Threshold uncertainty score0.036

Distilled classifier scores by category (both heads)

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 communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.012
GPT teacher head0.229
Teacher spread0.217 · 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

Citations0
Published2021
Admission routes1
Has abstractyes

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