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Record W4233045010 · doi:10.1063/1.3560385.1

10.1063/1.3560385.1

2011· dataset· en· W4233045010 on OpenAlexaff
Warren R. Dunn, Stavros Tavoularis

Bibliographic record

VenueDefault Digital Object Group · 2011
Typedataset
Languageen
FieldEngineering
TopicFluid Dynamics and Vibration Analysis
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsReynolds numberVortex sheddingShearing (physics)CylinderVortexFlow visualizationLaser Doppler velocimetryMechanicsPhysicsVelocimetryShear flowOpticsGeometryTurbulenceFlow (mathematics)Materials scienceMathematics

Abstract

fetched live from OpenAlex

Vortex-shedding patterns behind a step-cylinder in uniformly shear flow for Reynolds numbers between 152 and 674 were studied using flow visualization and laser Doppler velocimetry. The results were analyzed using spectral analysis and the wavelet transform. The effect of shearing on vortex shedding was found to be significant. As in uniform flow, the step-change in diameter caused the formation of a distinct near-step vortex cell behind the large cylinder, with frequency lower than those in both neighboring cells. Shearing affected both the spanwise length of this cell and the difference between its frequency and that in the adjacent large-cylinder cell. The values of these parameters were found to depend on both the Reynolds number and the orientation of the cylinder axis relative to the shear direction.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.022
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0060.014

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 head0.192
Teacher spread0.185 · 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; both teacher heads agree on what is shown here.

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
Published2011
Admission routes1
Has abstractyes

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