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Record W2186476323

Three-component, three-dimensional velocity measurement technique for micro- channel applications using a scanning µPIV

2010· article· en· W2186476323 on OpenAlexaff
David S. Nobes, Mona Abdolrazaghi, Darren Homeniuk

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicMicrofluidic and Bio-sensing Technologies
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsFrame rateOpticsCardinal pointChannel (broadcasting)Focus (optics)DetectorPlanarAcousticsPhysicsComputer science
DOInot available

Abstract

fetched live from OpenAlex

A micro-fluidic measurement technique capable of measuring three components of the velocity in the micro-channel has been developed in this paper. In the measurement system, a high speed CCD camera takes slices of planar (x,y) images in the micro channel while a piezoelectric scans the focal plane of an infinity corrected objective through the volume in the z-direction. The time series data set is reconstructed into individual scan volumes which are cross-correlated using a 3D algorithm. This leads to obtaining a near instantaneous three-dimensional velocity vector field. A benefit of this approach is that a large number of velocity vectors are generated to define the flow motion in comparison to the defocusing method. This technique has been designed for a specific micro-fluidic application that uses dielectrophoresis to levitate near-wall particles and to investigate mixing in micro-channel flows. Measurement uncertainty of the maximum out-of-plane velocity component depends on particle size, depth of focus of the system optics, frame rate of the single CCD detector and the scan rate of the piezo-electric scanning system.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.602
Threshold uncertainty score0.841

Codex and Gemma teacher scores by category

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

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 head0.040
GPT teacher head0.240
Teacher spread0.200 · 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 teacher head, not a consensus.

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

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

Citations1
Published2010
Admission routes1
Has abstractyes

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