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Record W4282928763 · doi:10.3390/w14121898

Experimental Studies on the Influence of Negatively Buoyant Jets on Flow Distribution in a 135-Degree Open Channel Bend

2022· article· en· W4282928763 on OpenAlexafffund
Xueming Wang, Colin D. Rennie, Abdolmajid Mohammadian

Bibliographic record

VenueWater · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicHydrology and Sediment Transport Processes
Canadian institutionsUniversity of Ottawa
FundersNatural Sciences and Engineering Research Council of CanadaChina Scholarship Council
KeywordsParticle image velocimetryOpen-channel flowMechanicsNozzleJet (fluid)Flow (mathematics)Secondary flowChannel (broadcasting)PhysicsDegree (music)GeologyEngineeringTurbulenceAcousticsThermodynamicsTelecommunications

Abstract

fetched live from OpenAlex

The present paper aims to investigate the evolution of velocity fields as well as secondary flows in an open channel bend under the influence of negatively buoyant jets. A 135-degree open channel bend was used for experiments, and the jet nozzle was located along the outer bank in the straight section upstream of the bend. Efforts were made to specify the flow structures with high precision measurements of three-dimensional velocities by means of a three-dimensional PIV (Particle Image Velocimetry) technology. The experimental results show that the jets comparatively affect the flow structure at the beginning and exit of the flow in a bend. Although the jets had little effect on the maximum streamwise velocity, complex secondary flow patterns and properties were found to be influenced due to the occurrence of the negatively buoyant jets in the bend.

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)0.000
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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.046
GPT teacher head0.275
Teacher spread0.229 · 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

Citations3
Published2022
Admission routes2
Has abstractyes

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