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Record W2076831767 · doi:10.1139/h11-108

Maximal accumulated oxygen deficit in running and cycling

2011· article· en· W2076831767 on OpenAlexvenueno aff
David W. Hill, Jakob L. Vingren

Bibliographic record

VenueApplied Physiology Nutrition and Metabolism · 2011
Typearticle
Languageen
FieldMedicine
TopicCardiovascular and exercise physiology
Canadian institutionsnot available
Fundersnot available
KeywordsCyclingTreadmillMathematicsAnaerobic exerciseOxygenVO2 maxCurvilinear coordinatesWork (physics)Animal scienceChemical oxygen demandStatisticsChemistryHeart rateEnvironmental engineeringEnvironmental sciencePhysical therapyEngineeringMedicineMechanical engineeringBiologyGeometryInternal medicineForestry

Abstract

fetched live from OpenAlex

The purpose of this study was to compare values of maximal accumulated oxygen deficit (MAOD; a measure of anaerobic capacity) in running and cycling. Twenty-seven women and 25 men performed exhaustive treadmill and cycle ergometer tests of ∼3 min, ∼5 min, and ∼7 min duration. Oxygen demands were estimated assuming a linear relationship between demand and intensity and also using upwardly curvilinear relationships. When oxygen demand was estimated using speed (with exponent 1.05), values for MAOD for the three running tests were virtually identical; the mean of the three values was 78 ± 7 mL·kg⁻¹. Use of an oxygen demand that was estimated using work rate (with exponent 1.00) generated the most similar values for MAOD from the three cycling tests (mean of 59 ± 6 mL·kg⁻¹). Consistent with the higher (p < 0.05) MAOD in running, peak post-exercise blood lactate concentrations were also higher (p < 0.05) in running (13.9 ± 2.2 mmol·L⁻¹) than in cycling (12.6 ± 2.4 mmol·L⁻¹). The results suggest that the relationship between oxygen demand and running speed is upwardly curvilinear for the speeds used to measure MAOD; the relationship between demand and cycle ergometer work rate is linear; MAOD is greater in running than in cycling.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.968
Threshold uncertainty score0.610

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.027
GPT teacher head0.253
Teacher spread0.226 · 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 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

Citations40
Published2011
Admission routes1
Has abstractyes

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