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Record W2800001681 · doi:10.1139/cjce-2017-0474

Impacts of turbulent flow over a channel bed with a vegetation patch on the incipient motion of sediment

2018· article· en· W2800001681 on OpenAlexaffvenue
Reza Shahmohammadi, Hossein Afzalimehr, Jueyi Sui

Bibliographic record

VenueCanadian Journal of Civil Engineering · 2018
Typearticle
Languageen
FieldEnvironmental Science
TopicHydrology and Sediment Transport Processes
Canadian institutionsUniversity of Northern British Columbia
Fundersnot available
KeywordsTurbulenceReynolds stressTurbulence kinetic energyVegetation (pathology)GeologySedimentSediment transportHydrology (agriculture)Open-channel flowChannel (broadcasting)BedformEnvironmental scienceGeomorphologySoil scienceMechanicsGeotechnical engineeringPhysics

Abstract

fetched live from OpenAlex

In this experimental study, the effect of a submerged vegetation patch with individual plants on the incipient motion of sediment has been examined. Results showed that a vegetation patch in the channel bed affected the incipient motion process of sediment and resulted in significant impacts on flow velocity, turbulence intensities, turbulent kinetic energy, and Reynolds stress distribution. The presence of vegetation patches completely change vertical distribution of velocity and Reynolds stress pattern and leads to negative Reynolds stress values in zones with negative velocity gradient. In the presence of a vegetation patch, threshold cross-averaged streamwise velocity is 20% smaller than that without vegetation, but because of the occurrence of the preferential path around the sheath section, the near-bed streamwise velocity is the same as without vegetation. Also, vegetation increases turbulence intensity, thus encouraging sediment motion. In the presence of a vegetation patch, the Shields parameter is, on average, one and half times that of without vegetation over the bed.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.782
Threshold uncertainty score0.972

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)0.0000.000

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 head0.006
GPT teacher head0.173
Teacher spread0.167 · 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

Citations27
Published2018
Admission routes2
Has abstractyes

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