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Record W3027302971 · doi:10.1017/jfm.2020.389

Instability and cross-boundary-layer transport by shoaling internal waves over realistic slopes

2020· article· en· W3027302971 on OpenAlexaff
Chengzhu Xu, Marek Stastna

Bibliographic record

VenueJournal of Fluid Mechanics · 2020
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicCryospheric studies and observations
Canadian institutionsUniversity of WaterlooUniversity of Calgary
Fundersnot available
KeywordsShoaling and schoolingInternal waveInstabilityMechanicsBoundary layerGeologyPhysicsOceanography

Abstract

fetched live from OpenAlex

Internal solitary and solitary-like waves (ISW) play an important role in mixing and sediment resuspension in naturally occurring stratified fluids, primarily through various instabilities and wave-breaking mechanisms. When shoaling into shallow waters, waves of depression may either fission into a packet of waves of elevation over mild slopes or break over steep slopes. The fissioning process is generally considered a less efficient transport and resuspension mechanism, compared to wave breaking, since very little turbulent mixing or energy dissipation occurs during this process. In the present work, however, we found that this is not always the case, at least in the particular context of ISW boundary-layer interaction. Using high-resolution numerical simulations performed in a domain representing a tilted laboratory tank, we found that boundary-layer instability in the form of a separation bubble consistently occurs during the fissioning process. The separation bubble is generated beneath the wave of elevation that emerges from the fissioning process, and is vitally influenced by currents induced by the leading wave of depression. As the waves shoal further, the growth and breakdown of the separation bubble leads to significant cross-boundary-layer transport. The results suggest that the fissioning process, which occurs over a considerable geographical region in the ocean, can be as efficient as wave breaking when it comes to cross-boundary-layer transport.

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 score0.193
Threshold uncertainty score0.959

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)0.0010.000

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 head0.027
GPT teacher head0.254
Teacher spread0.227 · 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

Citations32
Published2020
Admission routes1
Has abstractyes

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