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Record W4214812815 · doi:10.1063/5.0083765

Asymmetric Holmboe instabilities in arrested salt-wedge flows

2022· article· en· W4214812815 on OpenAlexafffund
Adam J. K. Yang, Edmund W. Tedford, Jason Olsthoorn, Gregory A. Lawrence

Bibliographic record

VenuePhysics of Fluids · 2022
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicOcean Waves and Remote Sensing
Canadian institutionsUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsPhysicsWavelengthWedge (geometry)AmplitudeMechanicsGrowth rateShear flowWave shoalingOpticsWave propagationMechanical waveLongitudinal waveGeometry

Abstract

fetched live from OpenAlex

The asymmetric Holmboe instabilities that form on an arrested salt wedge are investigated in the laboratory. The flow is characterized by three regions. Near the tip of the salt wedge, there is little wave activity; immediately downstream, mostly positive waves form; further downstream, both positive and negative waves are present. The appearance of these regions is determined by the spatial variation in the thickness of the saline layer, the shear layer thickness, and the offset between the density interface and velocity interface. We predict the growth of the instabilities by applying linear stability theory to the mean flow field. The predicted initial growth rate is consistent with the laboratory measurements until the Holmboe wave reaches a steepness ratio of 5–7 %. Then, the growth of the Holmboe wave is nonlinear. We found that the Holmboe wavelength increases downstream along the salt wedge. This wave stretching is related to the increase in the shear layer thickness and is supported by the gradual acceleration of the upper layer fluid. Eventually, the growth of the wave amplitude and the wave stretching balance a maximum wave steepness ratio of 10%. Although the wave is nonlinear, the predicted wave speed and wavelength are consistent with the laboratory measurements.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.568
Threshold uncertainty score0.373

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.017
GPT teacher head0.212
Teacher spread0.195 · 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

Citations8
Published2022
Admission routes2
Has abstractyes

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