MétaCan
Menu
Back to cohort
Record W4310718352 · doi:10.1063/5.0124569

Sensitivity of wave merging and mixing to initial perturbations in Holmboe instabilities

2022· article· en· W4310718352 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
TopicOceanographic and Atmospheric Processes
Canadian institutionsQueen's UniversityUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsPhysicsAmplitudePerturbation (astronomy)InstabilityWavenumberMechanicsSubharmonicQuantum electrodynamicsClassical mechanicsQuantum mechanicsNonlinear system

Abstract

fetched live from OpenAlex

Initial perturbations are commonly used in direct numerical simulations (DNS) to trigger the shear instability of stratified fluids. We investigate the effects of initial perturbations on the evolution of Holmboe instabilities with DNS. In particular, we model the interaction between a primary Holmboe wave and a subharmonic component that has a wavelength double that of the primary wave. We show that the phase difference and the amplitude of the primary and subharmonic components of the initial perturbation control the merging of Holmboe instabilities, which, in turn, influence diapycnal mixing in stratified flows. The amplitude difference has a more significant effect on the merging of Holmboe instabilities compared to the initial phase difference. For a given amplitude of the primary perturbation, a larger subharmonic perturbation results in an earlier merging event. In three-dimensional simulations, this preference of the subharmonic initial perturbation increased the amplitude of Holmboe waves by a factor of two. Although the subharmonic mode grows slower, it grows for longer producing more net mixing.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.114
Threshold uncertainty score0.228

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 head0.019
GPT teacher head0.223
Teacher spread0.204 · 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 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

Citations6
Published2022
Admission routes2
Has abstractyes

Explore more

Same venuePhysics of FluidsSame topicOceanographic and Atmospheric ProcessesFrench-language works237,207