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Record W2892331549 · doi:10.1051/e3sconf/20184002010

Quantification of bed-load transport over dunes

2018· article· en· W2892331549 on OpenAlexafffund
K.T. Lockwood, Patrick Grover, Ana Maria Ferreira da Silva

Bibliographic record

VenueE3S Web of Conferences · 2018
Typearticle
Languageen
FieldEnvironmental Science
TopicHydrology and Sediment Transport Processes
Canadian institutionsQueen's University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsFlumeShear stressBed loadSediment transportGeotechnical engineeringReynolds-averaged Navier–Stokes equationsGeologyMechanicsStress (linguistics)Reynolds stressShear (geology)Flow (mathematics)Computational fluid dynamicsSedimentGeomorphologyTurbulencePhysics

Abstract

fetched live from OpenAlex

There is disagreement in the literature as to whether a shear stress-based approach can be used to accurately predict sediment transport over dunes. This study aims to address this disagreement. To this end, use is made of an experiment involving the study of naturally formed, fully developed dunes produced in a laboratory flume. The bed shear stress is estimated through a combination of velocity, Reynolds stress measurements, and results of a CFD RANS rough wall model. The validity of using Bagnold’s equation to predict the bed-load rate is subsequently analyzed. In contrast to what has been previously suggested by some authors, it is found from the present experiment that the bed-load rate correlates well with the bed shear stress, and that Bagnold’s equation yields realistic values of the bed-load rate over the stoss side of the dune downstream of the reattachment point. This work also highlights the difficulties in reliably estimating the bed shear stress in complex flows. Such difficulties are overcome in this paper through a combination of flow velocity measurements and modeled results.

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 score0.228
Threshold uncertainty score0.989

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 studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0120.000

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 head0.019
GPT teacher head0.250
Teacher spread0.231 · 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

Citations1
Published2018
Admission routes2
Has abstractyes

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