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Record W2129834165 · doi:10.1113/eph8702246

EMG and Oxygen Uptake Responses During Slow and Fast Ramp Exercise in Humans

2002· article· en· W2129834165 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

fundA Canadian funder is recorded on the work.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueExperimental Physiology · 2002
Typearticle
Languageen
FieldEngineering
TopicMuscle activation and electromyography studies
Canadian institutionsnot available
FundersNational Heart, Lung, and Blood InstituteMedical Research CouncilMedical Research Council Canada
KeywordsCycle ergometerElectromyographyLactate thresholdLinear regressionVO2 maxRoot mean squareChemistryWork rateMathematicsInternal medicineAnimal scienceBlood lactateCardiologyMedicineHeart ratePhysical medicine and rehabilitationPhysicsBiologyBlood pressureStatistics

Abstract

fetched live from OpenAlex

This study examined the relationship between muscle recruitment patterns using surface electromyography (EMG) and the excess O(2) uptake (Ex.V(O(2))) that accompanies slow (SR, 8 W min(-1)) but not fast (FR, 64 W min(-1)) ramp increases in work rate (WR) during exercise on a cycle ergometer. Nine subjects (2 females) participated in this study (25 +/- 2 years, +/- S.E.M.). EMG was obtained from the vastus lateralis and medialis and analysed in the time (root mean square, RMS) and frequency (median power frequency, MDPF) domain. Results for each muscle were averaged to provide an overall response and expressed relative to a maximal voluntary contraction (%MVC). Delta.V(O(2))/DeltaWR was calculated for exercise below (S(1)) and above (S(2)) the lactate threshold (LT) using linear regression. The increase in RMS relative to the increase in WR for exercise below the LT (DeltaRMS/DeltaWR-S(1)) was determined using linear regression. Due to non-linearities in RMS above the LT, DeltaRMS/DeltaWR-S(2) is reported as the difference in RMS (DeltaRMS) and the difference in WR (DeltaWR) at end-exercise and the LT. SR was associated with a higher (P < 0.05) Delta.V(O(2))/DeltaWR (S(1), 9.3 +/- 0.3 ml min(-1) W(-1); S(2), 12.5 +/- 0.6 ml min(-1) W(-1)) than FR (S(1), 8.5 +/- 0.4 ml min(-1) W(-1); S(2), 7.9 +/- 0.4 ml min(-1) W(-1)) but a similar DeltaRMS/DeltaWR-S(1) (SR, 0.11 +/- 0.01% W(-1); FR, 0.10 +/- 0.01 % W(-1)). Ex.V(O(2)) was greater (P < 0.05) in SR (3.6 +/- 0.7 l) than FR (-0.7 +/- 0.4 l) but was not associated with a difference in either DeltaRMS/DeltaWR-S(2) (SR, 0.14 +/- 0.01% W(-1); FR, 15 +/- 0.02 % W(-1)) or MDPF (SR, 2.6 +/- 5.9 %; FR, -15.4 +/- 4.5 %). The close matching between power output and RMS during SR and FR suggests that the Ex.V(O(2)) of heavy exercise is not associated with the recruitment of additional motor units since Ex.V(O(2)) was observed during SR only. Compared to the progressive decrease in MDPF observed during FR, the MDPF remained relatively constant during SR suggesting that either (i) there was no appreciable recruitment of the less efficient type II muscle fibres, at least in addition to those recruited initially at the onset of exercise, or (ii) the decrease in MDPF associated with fatigue was offset by the addition of a higher frequency of type II fibres recruited to replace the fatigued motor units.

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.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.914
Threshold uncertainty score0.502

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0000.000

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 head0.013
GPT teacher head0.228
Teacher spread0.214 · 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