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Record W2790946617 · doi:10.2166/wst.2018.085

Experimental study of geysers through a vent pipe connected to flowing sewers

2018· article· en· W2790946617 on OpenAlexaff
Biao Huang, Shiqiang Wu, David Z. Zhu, Harry Edmar Schulz

Bibliographic record

VenueWater Science & Technology · 2018
Typearticle
Languageen
FieldEngineering
TopicWater Systems and Optimization
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsHead (geology)Hydraulic headDrainageGeologySurgeFlow (mathematics)Volumetric flow rateElectrical conduitPressure headSanitary sewerAtmospheric pressureEnvironmental scienceGeotechnical engineeringHydrology (agriculture)MechanicsGeomorphologyEnvironmental engineeringEngineering

Abstract

fetched live from OpenAlex

Geysers of air-water mixtures in urban drainage systems is receiving considerable attention due to public safety concerns. However, the geyser formation process and its relation with air release from pressurized pipes are still relatively little known. A large-scale physical model, that consisted of a main tunnel with a diameter of 270 mm and a length of 25 m connecting two reservoirs and a vertical vent pipe, was established to investigate geyser evolution and pressure transients. Experimental results including dynamic pressure data and high speed videos were analysed in order to characterize geysering flow through the vent pipe. Pressure transients were observed during geysering events. Their amplitudes were found to be about three times the driving pressure head and their periods were close to the classic surge tank predictions. The influence of flow rate and vent pipe size were examined: geyser heights and pressure peaks decreased for small flow rate and large diameter vent pipe. It is suggested that geyser heights are related with the pressure head and the density of the air-water mixture.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.011
GPT teacher head0.242
Teacher spread0.231 · 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 designBench or experimental
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

Citations25
Published2018
Admission routes1
Has abstractyes

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