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Record W4296204446 · doi:10.1029/2022wr032720

Impact of Density Gradients on the Secondary Flow Structure of a River Confluence

2022· article· en· W4296204446 on OpenAlexafffundabout
Jason Duguay, Pascale M. Biron, Jay Lacey

Bibliographic record

VenueWater Resources Research · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicHydrology and Sediment Transport Processes
Canadian institutionsUniversité de SherbrookeCollège de Maisonneuve
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsConfluenceFroude numberFront (military)TributarySecondary circulationFlow (mathematics)GeologyVortexGeometryOpen-channel flowClockwiseMechanicsLagrangian coherent structuresChannel (broadcasting)Secondary flowTurbulencePhysicsRotation (mathematics)MathematicsGeographyTelecommunications

Abstract

fetched live from OpenAlex

Abstract A small gradient in the densities (Δ ρ ) of two rivers in Canada was recently shown to develop coherent streamwise orientated vortices (SOVs) in their confluence. Here we use eddy‐resolved numerical modeling to examine how the magnitude and direction of Δ ρ affect these secondary flow structures. At equal density, lone anticlockwise SOVs are predicted near the surface, a possibility supported herein by recent aerial observations of such SOVs at the confluence. When a Δ ρ is considered, a front from the denser channel always slides underneath the lighter channel independent of whether the dense front pushes into the fast (Coaticook) or slow (Massawippi) tributary. When the fast Coaticook is denser, coherent clockwise rotating SOVs tend to form. However, when the slow Massawippi is denser, interfacial instabilities are generated as the fast flow of the Coaticook shears overtop the dense front. Thus, the tributary's velocity opposing the dense front's propagation modifies secondary flow characteristics. Importantly, both these cases would have the same densimetric Froude number ( F D ) if defined using an average velocity of the confluence (e.g., that of the downstream channel), yet this single F D value cannot account for the differences observed between both. This ambiguity inherent to current conventions for calculating F D places into question its use as an adequate predictor of density‐driven secondary flow at confluences.

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 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 score0.106
Threshold uncertainty score0.981

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0200.000

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 head0.022
GPT teacher head0.281
Teacher spread0.260 · 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

Citations20
Published2022
Admission routes3
Has abstractyes

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