Bibliographic record
Abstract
The term hemodynamics comes from the Greek words haima (blood) and dunamis (power) and refers to the movement and deformation (i.e., flow) of blood, and the forces that produce that flow. In this chapter we will examine this fascinating (and complex) topic. Everyone is familiar with blood's role as a transport medium: it carries oxygen and nutrients to metabolically active tissues, returns carbon dioxide to the lungs, delivers metabolic end-products to the kidneys, etc. However, the reader should be aware that blood does much more than simply deliver substances to target tissues. For example, it: provides a buffering reservoir to control the pH of bodily fluids serves as an important locus of the immune system transports heat, usually from centrally located tissues to distal ones, in order to help maintain a suitable temperature distribution throughout the body. Unfortunately, in this book we will not be able to examine all of these roles, and to a large extent we will simply view blood as a passive carrier, a fluid that transports physiologically important compounds within the body. However, within this context, it will soon become clear that something so “simple” as an analysis of blood flow as a transport mechanism is non-trivial. We begin by examining blood rheology. Blood rheology Rheology is the study of how materials deform and/or flow in response to applied forces. The applied forces are quantified by a quantity known as the stress , defined as the applied force per unit area.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.062 | 0.030 |
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".