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Record W2275503433 · doi:10.14796/jwmm.r246-09

Influence of Air Pocket Volume on Manhole Surge

2013· article· en· W2275503433 on OpenAlexvenueno aff
Steve Wright

Bibliographic record

VenueJournal of Water Management Modeling · 2013
Typearticle
Languageen
FieldEngineering
TopicHydraulic flow and structures
Canadian institutionsnot available
Fundersnot available
KeywordsSurgeVolume (thermodynamics)StormwaterCrown (dentistry)MechanicsStormwater managementStorm surgeGeologyEnvironmental scienceGeotechnical engineeringMeteorologyMarine engineeringHydrology (agriculture)EngineeringMaterials scienceGeomorphologyGeographyPhysicsComposite materialStormSurface runoffThermodynamicsBiology

Abstract

fetched live from OpenAlex

Large discrete air pockets trapped along the crown of a nearly horizontal stormwater tunnel have been shown in laboratory experiments to produce large vertical surges of water as the air is expelled through a vertical riser connected to the tunnel crown. The phenomenon has been hypothesized as the source of observed geysers from manholes in such systems. A previous theor-etical framework that assumes an unlimited supply of air has been shown to reproduce the essential details of laboratory experiments although deviations were observed in tests with smaller air volumes. However, it is clear that if the air volume is sufficiently small, reduced surges must occur. Previous experi-ments also indicate that riser diameter is a significant parameter with smaller diameters resulting in larger surges. Experiments were conducted in which the air pocket volume and riser diameter were systematically varied. Vertical surges (when normalized by the tunnel diameter) from ~0.2 to>25 were observed, confirming that larger surges were associated with larger air pocket volumes and smaller riser diam-

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)0.003
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.006
GPT teacher head0.182
Teacher spread0.177 · 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

Citations12
Published2013
Admission routes1
Has abstractyes

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