Three-component, three-dimensional velocity measurement technique for micro- channel applications using a scanning µPIV
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".