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Record W1974149058 · doi:10.1115/fedsm2008-55017

Development of Three-Dimensional Particle Tracking Velocimetry for the Investigation of Unsteady Flows

2008· article· en· W1974149058 on OpenAlexaff
Darren Homeniuk, David S. Nobes, David J. D. Wilson

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicParticle Dynamics in Fluid Flows
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsParticle tracking velocimetryVelocimetryParticle image velocimetryTracking (education)PlanarFlow (mathematics)Particle (ecology)MechanicsPhysicsClassical mechanicsComputer scienceGeologyTurbulenceComputer graphics (images)

Abstract

fetched live from OpenAlex

Instantaneous characterization of the three components of velocity of a fluid flow in a three-dimensional volume is difficult to accomplish under a variety of conditions. Symmetrical flows exhibit a measure of symmetry, which allow the use of a planar method. A method such as stereo particle imaging velocimetry could be used, which obtains three components of velocity in a planar sheet of the fluid. Flow asymmetries however, force the use of a fully three-dimensional approach to achieve a good understanding of the phenomenon. Particle tracking velocimetry is a method that allows three-dimensional information to be gathered instantaneously by determining the trajectories of individual particles. Limits exist, however, on this type of approach. This work examines the limits of a particle tracking velocimetry system that uses only two cameras to interrogate the flow.

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)0.003
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.003
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.004
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0020.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0010.001

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 head0.051
GPT teacher head0.239
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.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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

Citations0
Published2008
Admission routes1
Has abstractyes

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