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Record W2741083392 · doi:10.1139/cjce-2017-0274

A comparison of two-dimensional and three-dimensional flow structures over artificial pool-riffle sequences

2017· article· en· W2741083392 on OpenAlexaffvenue
Elham Fazel Najafabadi, Hossein Afzalimehr, Jueyi Sui

Bibliographic record

VenueCanadian Journal of Civil Engineering · 2017
Typearticle
Languageen
FieldEnvironmental Science
TopicHydrology and Sediment Transport Processes
Canadian institutionsUniversity of Northern British Columbia
Fundersnot available
KeywordsRiffleFlumeShear stressMechanicsFlow (mathematics)Reynolds numberGeometryShear (geology)GeologyMaterials scienceTurbulenceMathematicsPhysicsComposite material

Abstract

fetched live from OpenAlex

Experiments have been carried out in a flume with one 2D pool-riffle sequence and one 3D pool-riffle sequence, respectively. Objectives of this study are to determine whether or not the convergence of lateral flow exists. Variations of the near-bed shear stress have been studied. The characteristics of the secondary currents along a pool-riffle sequence have been investigated. Results showed that for the 3D pool-riffle sequence, the near-bed velocity decreases along convective deceleration flow (CDF) and increases along convective acceleration flow (CAF), respectively. It is found that the shear velocities estimated from the slope of the velocity gradient in the inner layer, decrease in the CDF section, and increase in the CAF section in the 3D pool-riffle sequences. The Reynolds shear stress is highest at the CDF section along longitudinal lines with distances of 10 cm and 20 cm away from the channel wall.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.015
GPT teacher head0.240
Teacher spread0.225 · 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 designSimulation or modeling
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

Citations8
Published2017
Admission routes2
Has abstractyes

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