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Record W2169863128 · doi:10.1061/40856(200)46

Three Dimensional Numerical Modeling of Cohesive Sediment Transport in a Shallow Oxbow Lake

2006· article· en· W2169863128 on OpenAlexfundno aff
Xiaobo Chao, Yafei Jia, F. Douglas Shields, Charlie Cooper

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicCoastal and Marine Dynamics
Canadian institutionsnot available
FundersCanada Excellence Research Chairs, Government of Canada
KeywordsGeologySediment transportSettlingSedimentDeposition (geology)Geotechnical engineeringWaves and shallow waterWind waveFlow (mathematics)Channel (broadcasting)Numerical modelsGeomorphologyTRACERMechanicsHydrology (agriculture)Numerical modelingEnvironmental scienceGeophysicsOceanographyEngineeringPhysics

Abstract

fetched live from OpenAlex

This paper presents the development and application of a three-dimensional numerical model for simulating the cohesive sediment transport in water bodies where both currents and wind-driven waves are important. The model was verified by a simple test case with an analytical solution (nonconservative tracer in a prismatic channel with uniform flow) and applied to Deep Hollow Lake, a small oxbow lake in Leflore County, Mississippi. The model produced predictions within 2% of the analytical solution. The bottom shear stresses induced by currents and waves were calculated, and the processes of resuspension, deposition and settling were considered. The primary forces associated with sediment transport were caused by wind-induced currents and waves. Simulated sediment concentrations were compared with limited field observations available, with generally good agreement. Simulated concentrations for a scenario with wind-driven waves were about one to three times greater than for a simulation without wind-wave processes.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.063
Threshold uncertainty score0.126

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.009
GPT teacher head0.184
Teacher spread0.174 · 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 source (direct Gemma or distilled Codex), 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

Citations0
Published2006
Admission routes1
Has abstractyes

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