A multi-domain model for microcirculation in optic nerve: blood flow and oxygen transport
Bibliographic record
Abstract
Microcirculation of blood and oxygen transport play important roles in biological function of optic nerve and are directly affected by damages or pathologies. This work develops a multi-domain model for optic nerve, that includes important biological structures and various physical mechanisms in blood flow and oxygen delivery. The two sets of vasculature network are treated as five domains in the same geometric region, with various exchanges among them (such as Darcy's law for fluid flow) and with the tissue domain (such as water leak, diffusion). The numerical results of the coupled model for a uniform case of vasculature distribution show mechanisms and scales consistent with literature and intuition. The effects of various important model parameters (relevant to pathological conditions) are investigated to provide insights into the possible implications. The vasculature distribution (resting volume fractions here) has significant impacts on the blood circulation and could lead to insufficient blood supply in certain local region and in turn affect the oxygen delivery. The water leak across the capillary wall will have nontrivial effects after the leak coefficients pass a threshold. The periodic arterial pressure conditions lead to expected periodic patterns and stable spatial profiles, and the uniform case is almost the averaged version of periodic case. The effects of viscosity, the stiffness of blood vessel wall, oxygen demand, etc. have also been analyzed. The framework can be extended to include ionic transport or to study the retina when more biological structural information is available.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.001 |
| 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".