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Record W3138887123 · doi:10.1029/2020jf005959

The Influence of Slipface Angle on Fluvial Dune Growth

2021· article· en· W3138887123 on OpenAlexaff
Suleyman Naqshband, David Hurther, Sanjay Giri, R. W. Bradley, Ray Kostaschuk, Jeremy G. Venditti, A.J.F. Hoitink

Bibliographic record

VenueJournal of Geophysical Research Earth Surface · 2021
Typearticle
Languageen
FieldEnvironmental Science
TopicHydrology and Sediment Transport Processes
Canadian institutionsSimon Fraser University
FundersNederlandse Organisatie voor Wetenschappelijk OnderzoekAgence Nationale de la Recherche
KeywordsGeologySedimentBed loadSediment transportFluvialGeomorphologySand dune stabilizationFlow (mathematics)TurbulenceFlow velocityBedformHydrology (agriculture)Geotechnical engineeringGeometryMechanicsPhysicsStructural basin

Abstract

fetched live from OpenAlex

Abstract Dunes dominate the bed of sandy rivers and they respond to flow by changing shape and size, modifying flow, and sediment transport dynamics of rivers. Our understanding of and ability to predict dune adaptation, particularly dune growth and decay, remain incomplete. Here, we investigate dune growth from an initial flatbed in a laboratory setting by continuously mapping the 3D bed topography using a line laser scanner combined with a 3D camera. High‐resolution profiles of flow velocity and sediment concentration providing both bedload and suspended sediment fluxes were obtained by deploying Acoustic Concentration and Velocity Profiler technology. Our analysis reveals that the magnitude of the dune slipface angle, which determines flow separation and controls turbulence production, adjusts to the imposed flow at time scales similar to the evolution of dune height and length. The initiation of a flow separation zone intensifies through scour, and results in acceleration of the dune growth. Gradients in sediment transport and the rate of dune growth are inherently linked to spatial variations in slipface angles. During dune growth, the slipface angle evolves differently than the ratio of dune height to length, which immediately reaches its equilibrium value after dune initiation.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.627
Threshold uncertainty score0.418

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.020
GPT teacher head0.295
Teacher spread0.276 · 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 designBench or experimental
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

Citations11
Published2021
Admission routes1
Has abstractyes

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