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Record W2762982302 · doi:10.1080/24748668.2017.1384975

Physical demand of wheelchair tennis match-play on hard courts and clay courts

2017· article· en· W2762982302 on OpenAlexaff
Matteo Ponzano, Massimiliano Gollin

Bibliographic record

VenueInternational Journal of Performance Analysis in Sport · 2017
Typearticle
Languageen
FieldMedicine
TopicSpinal Cord Injury Research
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsWheelchairHeart rateEnergy expenditureMathematicsAthletesPower (physics)Significant differencePsychologyStatisticsSimulationAnimal sciencePhysical therapyMedicineComputer sciencePolitical sciencePhysicsLawBiology

Abstract

fetched live from OpenAlex

The aim of this study was to define the performance model of wheelchair tennis, by means of 20 Hz GPS together with heart rate monitors, while also investigating potential surface-related differences. Hence, 12 matches performed by 12 nationally ranked wheelchair tennis players were examined. Each athlete played one match on the clay and one on the hard court according to a counterbalanced design, and the data regarding the parameters of maximum heart rate (HRmax), average heart rate (HRav), maximum speed (SPmax), average speed (SPav), maximum acceleration (ACCmax), maximum deceleration (DECmax), maximum metabolic power (MPmax), average metabolic power (MPav) and energy expenditure (EE) were analysed. The average match duration was 82 ± 16 min on clay courts (C) and 68 ± 17 min on hard courts (H) (p = .06), while the distance covered is greater, but not significant, on clay courts (C > H, +10%). The t-test did not highlight significant differences pertaining to the playing surface. The linear regression showed significant values concerning the distance covered (C: p < .0001, r2 = .82, H: r2 = .81) and the energy expenditure (C: p < .05, r2 = .5, H: r2 = .9). The playing surface does not affect the performance of competitive wheelchair tennis athletes.

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 score0.005
Threshold uncertainty score0.441

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
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 head0.025
GPT teacher head0.384
Teacher spread0.359 · 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

Citations13
Published2017
Admission routes1
Has abstractyes

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