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Record W2999780784 · doi:10.1061/9780784481424.015

Assessment and Design Optimization of Retrofitting Dropshaft with Air Circulation Pipes for Downstream Depressurization

2018· article· en· W2999780784 on OpenAlexaffabout
Yiyi Ma, David Z. Zhu, Jun Wei

Bibliographic record

VenueWorld Environmental and Water Resources Congress 2018 · 2018
Typearticle
Languageen
FieldEnvironmental Science
TopicUrban Stormwater Management Solutions
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsRetrofittingAir entrainmentCabin pressurizationEnvironmental scienceSanitary sewerDownstream (manufacturing)Entrainment (biomusicology)DrainageAirflowMarine engineeringPetroleum engineeringEngineeringEnvironmental engineeringCivil engineeringMechanical engineeringStructural engineering

Abstract

fetched live from OpenAlex

Plunging flow dropshafts in urban drainage systems have been reported to cause downstream air pressurization and subsequent sewer odor issues. Retrofitting dropshafts with air circulation pipes has been attempted to reduce sewer odour complaints in Edmonton, Alberta, Canada. In the current work, laboratory model study, field monitoring, and model prediction were conducted to assess the effectiveness of this retrofit and explore its design optimization. This retrofitting was found to be effective in reducing air entrainment of the dropshaft and depressurizing the downstream sewers. However, the study showed that horizontal air circulation pipes at various elevations contributed differently in reducing the air entrainment. Based on the air flow model, increasing the size of the horizontal pipes had a more significant effect on reducing the downstream air pressure compared to increasing the number of the pipes. It was found that the retrofitting can be optimized by removing the bottom horizontal air circulation pipes while increasing the pipe size.

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)0.000
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.090
Threshold uncertainty score0.512

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.010
GPT teacher head0.204
Teacher spread0.194 · 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 designObservational
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

Citations1
Published2018
Admission routes2
Has abstractyes

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