Blood Flow Analysis and Red Blood Cell Aggregation Investigation
Bibliographic record
Abstract
This work aims to develop a method to quantitatively analyze red blood cell (RBC) aggregates under controlled flow conditions, based on image processing. The set up consists of RBC suspensions entrained by a second fluid in a 150x33 μm microchannel. The experiments are performed by varying the hematocrit (10, 15 & 20%) and the flow rate (Q=5 & 10 μl/hr). The flow is visualized using a high speed camera coupled to a micro Particle Image Velocimetry (μPIV) system. Videos obtained with the high speed camera are processed using a MATLAB program to detect RBC aggregates based on the images intensities. An average aggregate size has been determined for each of the shear rates and hematocrits. The aggregates are shown to be larger at low flow rates and high hematocrits.
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How this classification was reachedexpand
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 | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".