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Record W2753282239 · doi:10.1139/cjce-2016-0228

Secondary currents induced mixing at channel confluences

2017· article· en· W2753282239 on OpenAlexaffvenue
Xue Chen, David Z. Zhu, P. M. Steffler

Bibliographic record

VenueCanadian Journal of Civil Engineering · 2017
Typearticle
Languageen
FieldEnvironmental Science
TopicHydrology and Sediment Transport Processes
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsConfluenceMixing (physics)Channel (broadcasting)MechanicsCurrent (fluid)Complete mixingTurbulencePerpendicularSecondary flowFlow (mathematics)Secondary circulationMixing patternsGeologyHydrology (agriculture)GeometryPhysicsGeotechnical engineeringMathematicsMass transferEngineeringTelecommunicationsComputer science

Abstract

fetched live from OpenAlex

Channel confluence is a common feature in river systems. The flow dynamics associated with channel confluence are highly three-dimensional with strong flow circulations and secondary currents and can result in enhanced river mixing downstream. In this study, a three-dimensional numerical model was employed to estimate the secondary currents induced streamline curvatures and the resulting mixing rate at channel confluences with different junction angles and discharge ratios. The results show that while twin secondary circulations are found at channel confluence, their contribution to the mixing depends on their local positions with respect to the river streams. With the secondary current growing downstream, the mixing rate is accelerated, in particular for the cases with the side channel perpendicular to the main channel and having a relatively large discharge. Turbulent diffusion can contribute up to about half of the rapid mixing. The mixing rates for different simulation cases are examined.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.122
Threshold uncertainty score0.998

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.013
GPT teacher head0.205
Teacher spread0.192 · 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.

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

Citations52
Published2017
Admission routes2
Has abstractyes

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