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Record W2887414792 · doi:10.1139/cjce-2017-0311

The role of waves on mixing in shallow waters

2018· article· en· W2887414792 on OpenAlexaffvenue
Shooka Karimpour, Vincent H. Chu

Bibliographic record

VenueCanadian Journal of Civil Engineering · 2018
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicOceanographic and Atmospheric Processes
Canadian institutionsMcGill UniversityYork University
Fundersnot available
KeywordsFroude numberMechanicsVorticityConvectionVortexMixing (physics)GeologyPhysicsMeteorologyAtmospheric sciencesClassical mechanicsFlow (mathematics)

Abstract

fetched live from OpenAlex

The role of waves on mixing is examined in a transverse shear flow in shallow waters for sub-critical, trans-critical, and super-critical flow over a range of convective Froude numbers. At low convective Froude numbers, the rollup of the vortex sheet to form an eddy defines the mixing. The mixing at higher convective Froude numbers, on the other hand, is affected by the shock waves and the radiation of the wave energy from an elongated vorticity element. Significant structural changes of the shear flow occur as the shock waves become discernible in the trans-critical and super-critical range of the convective Froude number. The shear layer growth is restrained while the momentum-thickness to vorticity-thickness ratio increases by a factor greater than 3. The fractional growth rate of the mixing layer in shallow waters and its dependence on the convective Froude number follows analogously the observed Mach-number dependence in gas dynamics.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.010

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 studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
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 head0.004
GPT teacher head0.150
Teacher spread0.146 · 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 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

Citations4
Published2018
Admission routes2
Has abstractyes

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