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Record W2160917259 · doi:10.1139/apnm-2015-0090

Maximal power output during incremental cycling test is dependent on the curvature constant of the power–time relationship

2015· article· en· W2160917259 on OpenAlexvenueno aff
Kristopher Mendes de Souza, Ricardo Dantas de Lucas, Paulo Cesar do Nascimento Salvador, Luiz Guilherme Antonacci Guglielmo, Renato Aparecido Corrêa Caritá, Camila Coelho Greco, Benedito Sérgio Denadai

Bibliographic record

VenueApplied Physiology Nutrition and Metabolism · 2015
Typearticle
Languageen
FieldMedicine
TopicSports Performance and Training
Canadian institutionsnot available
Fundersnot available
KeywordsCyclingMathematicsVO2 maxConstant (computer programming)Power (physics)Animal scienceInternal medicinePhysicsMedicineThermodynamicsHeart rateBiologyComputer science

Abstract

fetched live from OpenAlex

The aim of this study was to investigate whether the maximal power output (Pmax) during an incremental test was dependent on the curvature constant (W') of the power-time relationship. Thirty healthy male subjects (maximal oxygen uptake = 3.58 ± 0.40 L·min(-1)) performed a ramp incremental cycling test to determine the maximal oxygen uptake and Pmax, and 4 constant work rate tests to exhaustion to estimate 2 parameters from the modeling of the power-time relationship (i.e., critical power (CP) and W'). Afterwards, the participants were ranked according to their magnitude of W'. The median third was excluded to form a high W' group (HIGH, n = 10), and a low W' group (LOW, n = 10). Maximal oxygen uptake (3.84 ± 0.50 vs. 3.49 ± 0.37 L·min(-1)) and CP (213 ± 22 vs. 200 ± 29 W) were not significantly different between HIGH and LOW, respectively. However, Pmax was significantly greater for the HIGH (337 ± 23 W) than for the LOW (299 ± 40 W). Thus, in physically active individuals with similar aerobic parameters, W' influences the Pmax during incremental testing.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.866
Threshold uncertainty score0.322

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 head0.024
GPT teacher head0.249
Teacher spread0.225 · 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 designObservational
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

Citations10
Published2015
Admission routes1
Has abstractyes

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