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Record W2084175038 · doi:10.1002/esp.1928

The influence of dunes on mixing in a migrating salt‐wedge: Fraser River estuary, Canada

2010· article· en· W2084175038 on OpenAlexaffabout
Ray Kostaschuk, Jim Best, Paul V. Villard

Bibliographic record

VenueEarth Surface Processes and Landforms · 2010
Typearticle
Languageen
FieldEnvironmental Science
TopicHydrology and Sediment Transport Processes
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsGeologyFroude numberBedformEstuaryWedge (geometry)GeomorphologyHydrology (agriculture)Supercritical flowSediment transportSedimentGeotechnical engineeringOceanographyFlow (mathematics)Geometry

Abstract

fetched live from OpenAlex

Abstract Dune bedforms and salt‐wedge intrusions are common features in many estuaries with sand beds, and yet little is known about the interactions between the two. Flow visualization with an echosounder and velocity measurements with an acoustic Doppler current profiler over areas of flat‐bed and sand dunes in the highly‐stratified Fraser River estuary, Canada, were used to examine the effect of dunes on interfacial mixing. As the salt‐wedge migrates upstream over the flat‐bed, mixing is restricted to the lower portion of the water column. However, as the salt‐wedge migrates into the dune field from the flat bed, there is a dramatic change in the flow, and large internal in‐phase waves develop over each of the larger dunes, with water from the salt‐wedge reaching the surface of the estuary. The friction Richardson number shows that bed friction is more important in interfacial mixing over the dunes than over the flat‐bed, and a plot of internal Froude Number versus obstacle (dune) height shows that the salt‐wedge flow over the dunes is mainly supercritical. Such bedforms can be expected to cause similar effects in interfacial mixing in other estuaries and sediment‐laden density currents, and may thus be influential in fluid mixing and sediment transport. Copyright © 2010 John Wiley & Sons, Ltd.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.786
Threshold uncertainty score0.905

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 head0.003
GPT teacher head0.187
Teacher spread0.184 · 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 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

Citations13
Published2010
Admission routes2
Has abstractyes

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