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Record W2521550991 · doi:10.1515/johh-2015-0019

Turbulence characteristics of favorable pressure gradient flows in gravel-bed channel with vegetated walls

2015· article· en· W2521550991 on OpenAlexaff
Elham Fazel Najafabadi, Hossein Afzalimehr, Jueyi Sui

Bibliographic record

VenueJournal of Hydrology and Hydromechanics · 2015
Typearticle
Languageen
FieldEnvironmental Science
TopicHydrology and Sediment Transport Processes
Canadian institutionsUniversity of Northern British Columbia
Fundersnot available
KeywordsFlumeTurbulenceGeologyReynolds stressShear stressGeotechnical engineeringPressure gradientDragOpen-channel flowDrag coefficientMechanicsSediment transportFlow (mathematics)Hydrology (agriculture)GeomorphologySedimentPhysics

Abstract

fetched live from OpenAlex

Abstract Field observations showed that in many gravel-bed rivers, rice stems and gravel interact with each other and affect the flow structure. This calls to conduct research in more details in laboratory to better understand impacts of interaction between rice stems and gravel bed rivers on the flow structure. The outcome of such investigations can improve the estimation of drag coefficient in hydrodynamic models. Experiments were carried out in a flume with gravel bed and the vegetated vertical flume walls to investigate turbulence characteristics under favorable pressure gradient flows. Results showed that the stress fraction involves both the sweep and ejection dominance near the vegetated flume walls, showing no negative Reynolds shear stress near water surface. The values of exuberance ratio play a significant role on the magnitude of the Reynolds shear stress which varies with the distance from the vegetation. Quadrant analysis reveals that a major part of the momentum flux is transported during a short period of strong turbulence activity near the bed at the central axis. Augmentation of the hole size makes difference between contributions of sweep and ejection with those of the outward and inward interactions near the bed, however, the hole size doesn’t play any role near the water surface.

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)0.001
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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.407
Threshold uncertainty score0.581

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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)0.0000.000

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 head0.010
GPT teacher head0.200
Teacher spread0.190 · 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 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

Citations12
Published2015
Admission routes1
Has abstractyes

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