A streak length‐based method for quantifying red blood cell flow in skeletal muscle arteriolar networks
Bibliographic record
Abstract
Abstract Objective To develop an experimental method to quantify RBC flow throughout skeletal muscle arteriolar networks. Methods Data on arteriolar geometry were obtained using IVVM of the rat GM. RBC velocities and number densities were also obtained during these experiments using fluorescently labeled RBCs. Arteriolar and RBC data were combined to estimate blood volume flow rates, HT and HD values, and RBC volume flow rates. Validation of hematocrit and RBC flow results was performed at arteriolar bifurcations using both mass balance and comparisons to an established model of the PS effect. Results Estimated HT values were within the expected range (6%‐34%) for the arterioles considered (29‐130 μm). RBC mass balance error was 18 ± 16% (mean ± SD, n = 7 bifurcations). RBC outflow from diverging bifurcations as a function of RBC inflow was given by Y = 0.986*X + 0.331 with R2 = 0.987. Outflow HT as a function of the PS prediction was given by Y = 1.034*X + 0.004 with R2 = 0.691. RBC outflow as a function of the prediction was given by Y = 0.917*X + 0.804 with R2 = 0.891. Conclusions An experimental method has been developed and validated that can easily and accurately quantify RBC flow distribution in large skeletal muscle arteriolar networks and provides direct estimates of HT values.
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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 | 0.002 |
| 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 | 0.000 | 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".