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Record W4320477123 · doi:10.1061/jhend8.hyeng-13238

Modeling River Plume Dynamics in a Large Wind-Forced Embayment

2023· article· en· W4320477123 on OpenAlexaff
Dina H. Elbagoury, Leon Boegman, Yerubandi R. Rao

Bibliographic record

VenueJournal of Hydraulic Engineering · 2023
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicCoastal and Marine Dynamics
Canadian institutionsEnvironment and Climate Change CanadaQueen's University
Fundersnot available
KeywordsPlumeFroude numberAdvectionShoreHydrology (agriculture)PanacheEnvironmental scienceGeologyRiver mouthBayPrevailing windsWind speedCurrent meterWater qualityBuoyancyOceanographyGeomorphologySedimentMeteorologyFlow (mathematics)

Abstract

fetched live from OpenAlex

Water quality degradation, in the form of undesirable algae, occurs near the Nottawasaga River mouth and along Wasaga Beach, southeastern Georgian Bay. The ability to manage this water resource is compromised due to lack of monitoring and science. In the present study, a 3D hydrodynamic model was applied to gain an understanding of how advection and dilution of the river plume can trap nutrients along Wasaga Beach. Simulated water temperatures and currents had a root-mean-square error between 1.5°C and 2.5°C and ∼0.06 m s−1, respectively. Maximum nearshore river concentrations occurred near the beach during high river discharge >20 m3 s−1 and westerly winds. Generally, the river plume was advected by winds >∼4 m s−1, traveling along the northern shoreline during southerly winds and along the southwest shoreline during northeasterly winds. This process was analytically modeled when the wind strength indicator or Froude number (ratio of the characteristic wind-velocity scale to the buoyancy–velocity scale) was greater than one.

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.017
Threshold uncertainty score0.386

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 head0.007
GPT teacher head0.191
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 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

Citations3
Published2023
Admission routes1
Has abstractyes

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