Comprehensive <i>In Situ</i> Analysis of Arteriolar Network Geometry and Topology in Rat Gluteus Maximus Muscle
Bibliographic record
Abstract
OBJECTIVES: To provide detailed geometric and topological descriptions of the rat gluteus maximus arteriolar network, and to measure the distribution of diameters and lengths as well as their associated variability within and between networks. METHODS: Complete arteriolar networks arising from feed artery (inferior gluteal artery) to terminal branches were imaged under baseline conditions, using IVVM. Photomontages of complete networks were assembled and evaluated offline for measurements of geometry and topology. Single-line (skeletonized) tracings of the networks were made for fractal analysis. RESULTS: Diameters and lengths decreased with increasing topological order (centrifugal), while number of elements increased with increasing order. Horton's laws were shown to be valid within the arteriolar networks of the rat GM. Inter-network variability in diameter (~5-22%) and length (~17-30%) at each order was generally lower than the corresponding intra-network variability in diameter (~10-48%) and length (~39-106%). CONCLUSIONS: Data presented in this study provide crucial quantitative analysis of complete arteriolar networks within healthy skeletal muscle, and may serve as ideal experimental inputs for future theoretical studies of skeletal muscle microvascular structure and function.
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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 | 0.000 |
| 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".