Regulation of Blood Flow at the Onset of Exercise by Feed Forward and Feedback Mechanisms
Bibliographic record
Abstract
Blood flow adapts quickly after the onset of exercise to meet the metabolic demands of skeletal muscle. This review approaches the issue of how rapidly blood flow adapts and what the mechanisms for adaptation are primarily from a control theory perspective. Several recent papers have suggested that O2 transport proceeds at a rate that anticipates the metabolic demand over a very wide range of work rates. When considered from a control theory perspective, this implies involvement of feed forward control. Although there is one very important feed forward mechanism in the muscle pump that is activated with the onset of exercise, other evidence suggests that adjustment of blood flow to match the metabolic demand relies on feedback control from local dilator factors released in proportion to the metabolic demand. These distinct mechanisms with different onset times mean that blood flow adapts to the exercise demand with at least two distinct phases. The time course of the adaptation varies greatly between work rates, showing that blood flow control cannot be described by a linear control system and that the mechanisms responsible for vasodilation are dependent on work rate.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".