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Record W2027462847 · doi:10.1519/jsc.0b013e3182472fc3

Comparison of Two Aerobic Field Tests in Young Tennis Players

2011· article· en· W2027462847 on OpenAlexaff
Marie-Agnès Fargeas-Gluck, L Léger

Bibliographic record

VenueThe Journal of Strength and Conditioning Research · 2011
Typearticle
Languageen
FieldMedicine
TopicSports Performance and Training
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsAerobic exerciseVO2 maxAerobic capacitySprintMathematicsHeart ratePhysical therapyMedicineInternal medicine

Abstract

fetched live from OpenAlex

This study compares the maximal responses of a new aerobic tennis field test, the NAVTEN to a known aerobic field test, often used with young tennis players, that is, the continuous multistage 20-m shuttle run test (20-m SRT). The NAVTEN is an intermittent (1-minute/1-minute) multistage test with side-to-side displacements and ball hitting. Ten young elite tennis players aged 12.9 ± 0.3 (mean ± SD) randomly performed both tests and were continuously monitored for heart rate (HR) and oxygen uptake (V[Combining Dot Above]O2) using the Vmax ST (Sensormedics). The 20-m SRT and NAVTEN show similar HRpeak (202 ± 6.1 vs. 208 ± 9.5, respectively) and V[Combining Dot Above]O2peak (54.2 ± 5.9 vs. 54.9 ± 6.0 ml·kg·min). Pearson correlations between both tests were 0.88 and 0.92 for V[Combining Dot Above]O2peak and maximal speed, respectively. The NAVTEN yielded V[Combining Dot Above]O2peak values that are typical for active subjects of that age and are similar to the 20-m SRT supporting its use to measure aerobic fitness of young tennis players in specific and entertaining field conditions. The fact that two-thirds of the tennis players achieved a different ranking (±1 rank) with the NAVTEN and the 20-m SRT suggests that the NAVTEN may be more specific than the 20-m SRT to assess aerobic fitness of tennis players. From a practical point of view, the NAVTEN test is more specific and pedagogical for young tennis players even though both tests yield similar maximal values.

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)0.001
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.022
Threshold uncertainty score0.260

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 integrity0.0000.001
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 head0.106
GPT teacher head0.417
Teacher spread0.312 · 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

Citations18
Published2011
Admission routes1
Has abstractyes

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