Effect of depth of correlation on cross-correlation blood flow measurements in glass microchannels
Bibliographic record
Abstract
The aim of this study was to evaluate the effect of the depth of correlation (DOC) on the cross-correlation method (CC) applied to microcirculatory blood flow in vitro. The cross-correlation algorithm was optimized to compute red blood cell velocity profiles in tube flow. Flow rates, estimated by computing the circular integral of mean velocity profiles, were compared with calibrated pump flows for different focus planes of the microscope and different flow rates. Results show a mean flow underestimation of 2.8 plusmn 5% for all positions of the focus plane inside the tube diameter, highlighting the non-negligible DOC effect when CC is applied to study blood microcirculation. An underestimation of 1% was found with an optimal focus plane. To conclude, flow rate estimation in microcirculatory blood flow can be accurate if the DOC effect is properly compensated for by the CC flow estimation method.
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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 | 0.000 |
| 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".