Capillary Network Morphology and Capillary Flow
Bibliographic record
Abstract
This paper examines the authors' research on capillary network morphology and the heterogeneity of capillary red cell (RBC) perfusion in skeletal muscle with the aim of demonstrating that capillary network structure plays a major role in determining flow distribution. Capillary network morphology was examined by quantifying the heterogeneity of capillary diameters, path and segment lengths, as well as the changes in configuration that occur as vessels accommodate themselves to continual changes of fiber length. Because of the network complexity and the two-phase nature of the perfusing blood, both spatial (i.e. among capillaries) and temporal heterogeneity of capillary perfusion were predicted to result. By means of computer analysis of video images of the microcirculation in vivo, we have demonstrated that more than 70% of the total spatial heterogeneity of capillary RBC perfusion arises from the capillary network as opposed to the arterioles, and that RBC flow continuously redistributes among capillaries. The spatial heterogeneity increases substantially as the arteriolar input to the network falls, and the data predict that during low-flow states, the network will fail to distribute blood properly among its constituent vessels. Thus passive rheological mechanisms and capillary network morphology are important determinants of functional capillary density.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".