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Record W2133424693 · doi:10.1139/h03-058

Regulation of Blood Flow at the Onset of Exercise by Feed Forward and Feedback Mechanisms

2003· review· en· W2133424693 on OpenAlexafffund
Richard L. Hughson

Bibliographic record

VenueCanadian Journal of Applied Physiology · 2003
Typereview
Languageen
FieldMedicine
TopicCardiovascular and exercise physiology
Canadian institutionsUniversity of Waterloo
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsBlood flowMetabolic adaptationDilatorVasodilationMetabolic regulationMechanism (biology)Metabolic rateWork rateMetabolic control analysisWork (physics)Skeletal muscleInternal medicineMedicineHeart rateMetabolismEngineeringBlood pressure

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.868
Threshold uncertainty score0.952

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.011
GPT teacher head0.231
Teacher spread0.220 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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".

Quick stats

Citations35
Published2003
Admission routes2
Has abstractyes

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