A mechanical model of cerebral circulation during sustained acceleration.
Bibliographic record
Abstract
BACKGROUND: High positive Gz may result in inadequate blood supply to the brain even if the central blood pressures are maintained at normal levels. We use a mechanical model to simulate the influence of sustained +Gz on cerebral circulation. METHODS: The model consists of ascending and descending tubes representing the extracranial arteries and veins, respectively, and a cranium in which the tubes are enclosed within water-filled rigid container to account for the skull and the cerebrospinal fluid. A thick-walled Tygon tube and a thin-walled surgical drain tube were used for the arteries and veins, respectively. The flow of water was driven by a pressure difference at the model ends, and the change in the gravitational vector was accomplished by tilting the model. RESULTS: The flow drops with an increasing tilt angle only if the descending arm collapses. However, when the pressures at the model ends are sufficiently elevated, the flow is restored to normal value. In the cranium model, the pressure in the water surrounding the tubes always stays close to the pressure in the surgical tubing. Consequently, the tubes in the container do not collapse. CONCLUSIONS: The principal effect of Gz on flow through the model occurs via changes in the resistance of the collapsed descending arm. As the pressures at the model ends are elevated, the descending arm opens and the flow increases. The pressure in the cranium model is dictated by the condition that the volume of the container has to remain constant.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".