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Record W2157182075 · doi:10.1109/ccm.1995.516171

The combined velocity-density-vorticity (VDV) sensor: a report on its first use

2002· article· en· W2157182075 on OpenAlexaffabout
A. Trivett, Christopher A. Bowers, Anthony Bowen, John T. Snow, Michael G. Skafel

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicUnderwater Acoustics Research
Canadian institutionsDalhousie University
Fundersnot available
KeywordsFlumeVorticityMeasure (data warehouse)BuoyancyFlow (mathematics)Marine engineeringFlow velocityFlow measurementFlux (metallurgy)BuoyAcousticsGeologyEnvironmental scienceMechanicsPhysicsEngineeringVortexComputer scienceMaterials scienceData mining

Abstract

fetched live from OpenAlex

the authors have developed a general-purpose sensor to measure several flow properties simultaneously. The instrument is very compact, simple to use, accurate, and cost-effective. VDV can measure 3-dimensional velocity, buoyancy flux and vorticity using advanced ultrasonics. The measurements are simultaneous and are sampled in the same volume of water. This guarantees very high correlation between the velocity, density and vorticity data. This is essential in energetic, rapidly fluctuating coastal environments, as well as in some low-speed deep ocean applications. The authors have completed a series of tests in a large laboratory tow-tank and in a wave flume at the Canada Centre for Inland Waters. This paper reports on the initial results of the tests.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.644
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.003

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 head0.080
GPT teacher head0.259
Teacher spread0.179 · 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 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

Citations2
Published2002
Admission routes2
Has abstractyes

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