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Record W2148344116 · doi:10.1155/2010/969407

An Assessment of the Literature and Recent Developments on the Shallow Wake in an Open Channel Flow

2010· article· en· W2148344116 on OpenAlexaff
Arindam Singha, Ram Balachandar

Bibliographic record

VenueAdvances in Civil Engineering · 2010
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Turbulent Flows
Canadian institutionsUniversity of Windsor
Fundersnot available
KeywordsWakeDragFlow (mathematics)Waves and shallow waterGeologyOpen-channel flowMobilitiesChannel (broadcasting)Lagrangian coherent structuresMechanicsComputer sciencePhysicsSociologyTelecommunicationsOceanography

Abstract

fetched live from OpenAlex

Although shallow flows are very abundant both in nature and in flows of technological importance, their effective characterization started only recently. This type of flow, being vertically confined, exhibits some unique features, which are absent in conventional unconfined flows. A brief review of the related research articles indicates that the exploration of the shallow wake had occurred in three distinct chronological phases. Initially, almost all investigations were aimed at exploring the differences between shallow flows and unconfined flows. Given the differences, the second phase of investigations was mostly concerned with identifying the sources of these differences found already. The third and most recent group of investigations attempted to correlate the flow topology with the extent of the confinement and focused on more applied areas like control of wake instability and consequent effects on drag reduction, and the effect of the coherent structures on species transport. The present paper is intended to present retrospect of the seminal investigations regarding shallow flows, and summarizes the key results from widely ranging studies. Another motivation of the present paper is to provide a basis for future research of shallow wake.

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 score0.059
Threshold uncertainty score0.447

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 head0.006
GPT teacher head0.255
Teacher spread0.249 · 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

Citations5
Published2010
Admission routes1
Has abstractyes

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