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Record W1516815749 · doi:10.5432/ijshs.1.9

Blood Flow and Metabolic Control at the Onset of Heavy Exercise

2003· article· en· W1516815749 on OpenAlexaff
Richard L. Hughson, Heleen Schijvens, Shannon Burrows, Deanna Devitt, Andrew C. Betik, Maria T. E. Hopman

Bibliographic record

VenueInternational Journal of Sport and Health Science · 2003
Typearticle
Languageen
FieldMedicine
TopicCardiovascular and exercise physiology
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsIncremental exerciseCycle ergometerBlood flowCardiologyHeart ratePhysical exerciseDoppler ultrasoundInternal medicineVO2 maxMedicinePhysical therapyBlood pressure

Abstract

fetched live from OpenAlex

The rate of increase in oxygen uptake (VO2) at the onset of a bout of heavy exercise is faster if it is preceded by a similar bout of heavy exercise. We tested the hypothesis during heavy leg exercise that leg blood flow (LBF) and VO2 would both be elevated during the adaptive phase. On three separate days, six healthy young men completed two bouts of 6-minutes of knee extension / flexion exercise at about 85% VO2peak separated by 5-minutes 0-watt exercise on an electrically braked ergometer. LBF was determined by Doppler ultrasound. In the second exercise bout, LBF and VO2 were significantly elevated in the baseline before exercise and throughout the exercise. Both the mean response time (time to 63% of difference between baseline and calculated end value) and the difference in VO2 between minutes 3 and 6 of exercise indicated significantly faster attainment of the end exercise value in the second heavy exercise bout. These data showing the elevated LBF in the second bout of heavy exercise support the link between O2 delivery and the adaptation of oxidative metabolism at the onset of heavy exercise.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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)0.0010.000

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 head0.016
GPT teacher head0.312
Teacher spread0.297 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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

Citations20
Published2003
Admission routes1
Has abstractyes

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