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Record W4253962324 · doi:10.1063/1.4895645.1

10.1063/1.4895645.1

2014· dataset· en· W4253962324 on OpenAlexaff
James Munroe, Bruce Sutherland

Bibliographic record

VenueDefault Digital Object Group · 2014
Typedataset
Languageen
FieldEarth and Planetary Sciences
TopicOcean Waves and Remote Sensing
Canadian institutionsUniversity of AlbertaMemorial University of Newfoundland
Fundersnot available
KeywordsTurbulenceTurbulence kinetic energyMechanicsInternal waveParticle image velocimetrySchlierenPhysicsBaroclinityKinetic energyOpticsClassical mechanics

Abstract

fetched live from OpenAlex

We examine mixed-layer deepening and the generation of internal waves in stratified fluid resulting from turbulence that develops in response to an applied surface stress. In laboratory experiments the stress is applied over the breadth of a finite-length tank by a moving roughened conveyor belt. The turbulence in the shear layer is characterized using particle image velocimetry to measure the kinetic energy density. The internal waves are measured using synthetic schlieren to determine their amplitudes, frequencies, and energy density. We also perform fully nonlinear numerical simulations restricted to two dimensions but in a horizontally periodic domain. These clearly demonstrate that internal waves are generated by transient eddies at the integral length scale of turbulence and which translate with the background shear along the base of the mixed layer. In both experiments and simulations we find that the energy density of the generated waves is 1%–3% of the turbulent kinetic energy density of the turbulent layer.

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)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.849
Threshold uncertainty score0.507

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0040.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0030.006
Science and technology studies0.0010.001
Scholarly communication0.0030.002
Open science0.0040.003
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.1510.369

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 head0.195
Teacher spread0.188 · 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.

Study designNot applicable
Domainnot available
GenreDataset

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

Citations0
Published2014
Admission routes1
Has abstractyes

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