Micro-particle image velocimetry for blood flow in thick round glass micro-channels: Channel fabrication and velocity profile characterization
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Bibliographic record
Abstract
This method describes the use of thick round borosilicate glass micro-channels for blood flow visualization using micro-particle image velocimetry (µPIV) techniques. In contrast with popular methods using squared polydimethylsiloxane channels, this method allows for visualization of blood flow in channel geometries that resemble more the natural physiology of human blood vessels. With a custom designed enclosure, the microchannels were submerged in glycerol to reduce light refraction occurring during µPIV due to the thick walls of the glass channels. A method is proposed to correct the extracted velocity profiles from the µPIV to account for out-of-focus error. The customized elements of this method include: • The use of thick circular glass micro-channels, • a custom designed mounting solution for the channels on a glass slide for flow visualization, • a MATLAB code to correct velocity profile accounting for out-of-focus error.
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Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it