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Record W4401822343 · doi:10.2166/wst.2024.291

Blockage dynamics in sewer pipes: investigating snagging, accumulation, and dissipation rates of wet wipes

2024· article· en· W4401822343 on OpenAlexaff
Katayoun Kargar, Darko Joksimovic

Bibliographic record

VenueWater Science & Technology · 2024
Typearticle
Languageen
FieldEnvironmental Science
TopicUrban Stormwater Management Solutions
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsSanitary sewerDissipationEnvironmental scienceVolumetric flow rateCombined sewerFlow (mathematics)Environmental engineeringSurface runoffEcologyStormwaterMechanicsBiology

Abstract

fetched live from OpenAlex

ABSTRACT The increase in sewer blockages, exacerbated by the COVID-19 pandemic due to improper disposal of non-biodegradable items like wet wipes and sanitary products, along with fats, oils, grease, and the presence of defects in sewers, underscores the need for a more profound understanding of wipes’ contribution to these blockages. This study investigated the probability of wipe snagging on sewer imperfections and their subsequent accumulation and dissipation rates under various conditions through laboratory experiments. Findings indicated a significant variability in the likelihood of wipes snagging (ranging from 93% to 0) and accumulating (ranging from 71% to 0), which was influenced by the defect location and the sewer flow rate. Moreover, the study highlighted a direct relationship between the flow rate in the sewer and the rate at which wipes dissipate. Conversely, an inverse relationship was observed between the size of blockages and the dissipation rate of wipes, with larger blockages typically reducing the speed of this process. Importantly, while dissipation rates at low flow rates are nearly constant regardless of the number of wipes and duration, at medium to high flow rates, dissipation initially increases but levels off after 24 h, demonstrating the persistence of wipe-caused blockages in small-diameter sewer pipes.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.707
Threshold uncertainty score0.657

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.002
Scholarly communication0.0000.001
Open science0.0000.001
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 head0.017
GPT teacher head0.274
Teacher spread0.257 · 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

Citations4
Published2024
Admission routes1
Has abstractyes

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