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Record W2400667534 · doi:10.1063/1.4948544

Mass transport by mode-2 internal solitary-like waves

2016· article· en· W2400667534 on OpenAlexaff
David Deepwell, Marek Stastna

Bibliographic record

VenuePhysics of Fluids · 2016
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicOceanographic and Atmospheric Processes
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsPycnoclinePhysicsInternal waveMass transportTRACERPotential vorticityThermal diffusivityMechanicsVorticityVortexThermodynamicsGeologyNuclear physicsOceanography

Abstract

fetched live from OpenAlex

We present the first three-dimensional numerical simulations of the mass transport capabilities of mode-2 waves formed by a lock-release mechanism with both single and double pycnocline stratifications. Single pycnoclines and double pycnoclines with a small spacing between the pycnocline centres were found to exhibit large Lee instabilities which formed during the collapse of the intermediate density region. These instabilities led to the generation of vorticity dipoles across the mid-depth, and thereby contributed to the reduction in the mass transported by the wave. A double pycnocline with a separation of approximately 12% of the depth between the two pycnocline centres was found to transport a passive tracer optimally for the longest time-period. Increasing Schmidt number correlated with increasing mass transport, while decreasing the tracer diffusivity led to increasing mass transport, but only when a trapped core existed. Contrasted two-dimensional simulations reveal that in certain cases, most noticeably the optimal transport case, the mass transport is significantly different from the corresponding three-dimensional simulation.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.012
Threshold uncertainty score0.024

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.000
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 head0.007
GPT teacher head0.199
Teacher spread0.192 · 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 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

Citations29
Published2016
Admission routes1
Has abstractyes

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