An algorithm for generating biophysically realistic three‐dimensional arteriolar networks applied to rat skeletal muscle
Bibliographic record
Abstract
Abstract The microcirculation comprises small vessel networks that regulate blood perfusion within tissues. The relationship between tissue shape or size and its microvascular properties is not yet clear. This study develops an algorithm for computationally simulating branching arteriolar networks within ellipsoidal tissue volumes, including user‐adjustable parameters (e.g., tissue width‐length‐height dimensions and microvessel density) for application within different rodent skeletal muscles. The algorithm is developed using principles from constrained constructive optimization, an iterative network generation framework based on proposed mechanisms of vascular growth. Networks generated within muscles of varying shapes and sizes were analyzed over a range of geometric (e.g., mean diameter, length, and number of bifurcations per Strahler's and centrifugal order, fractal dimension) and hemodynamic (e.g., Murray's law exponent, hematocrit) properties. Statistical similarity was observed across different skeletal muscle tissues, with differences due to tissue shape being observed only above a vessel diameter threshold of ~25 μm (varying at large or small tissue volumes at the scale m 3 or mm 3 ). The algorithm was comprehensively validated against in vivo data using different modeling approaches (whole tissue vs. subsection simulations). The algorithm's accuracy and adaptability support a wide range of research objectives and contributes to advancing current understanding of perfusion distribution in healthy tissue.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".