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Record W4402088126 · doi:10.2166/wst.2024.298

Experimental investigation of erosion and transport of binary sediment in sewer pipes

2024· article· en· W4402088126 on OpenAlexafffund
Run Zhang, David Z. Zhu, Dave Chan, Bert van Duin

Bibliographic record

VenueWater Science & Technology · 2024
Typearticle
Languageen
FieldEnvironmental Science
TopicUrban Stormwater Management Solutions
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsErosionSedimentSediment transportEnvironmental scienceHydrology (agriculture)GeologyGeotechnical engineeringGeomorphology

Abstract

fetched live from OpenAlex

ABSTRACT Understanding sediment transport in storm sewer systems is essential to prevent pipe blockages, associated flooding, and maintenance. In this research, a laboratory study was conducted in a pipe to investigate the erosion and transport process of a binary sediment, i.e., a material with two different particle sizes. The sediment used in this study was a mixture of two sizes of spherical glass beads (0.8 and 4 mm) and two sizes of natural soil (sand and gravel), with different amounts of small size particles. The results show that the critical velocities for the initiation of sediment transport increase linearly with a decrease in the fine particle content of the mixture. A method to predict the critical velocity of a binary mixture is proposed based on the fine content and the critical velocity of single-size large particle and single-size small particle sediments. The transport rate of the mixture lies between that of the single-size large sediment and single-size small sediment. The transport rate is increased with an increase in the fine content, which means that mixing small particles with large particles can significantly increase the transport rate of large particles and the total transport rate.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.042
Threshold uncertainty score0.764

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.002
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 head0.009
GPT teacher head0.217
Teacher spread0.208 · 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 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

Citations1
Published2024
Admission routes2
Has abstractyes

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