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Record W2961947988 · doi:10.12775/pps.2019.05.01.001

Maximal aerobic speed as a useful tool to understand specific training demand among elite male volleyball

2019· article· en· W2961947988 on OpenAlexaboutno aff
Louglaib Lakhdar, Zerf Mohammed

Bibliographic record

VenuePedagogy and Psychology of Sport · 2019
Typearticle
Languageen
FieldMedicine
TopicSports Performance and Training
Canadian institutionsnot available
Fundersnot available
KeywordsElitePhysical medicine and rehabilitationPlyometricsPsychologyMatch playJumpingJumpPhysical therapyExplosive strengthBackupApplied psychologyComputer scienceMedicine

Abstract

fetched live from OpenAlex

Aim: The study aims to study the impact of Maximal Aerobic Speed (MAS) levels on the growth of skills fitness among volleyball. Methods: To achieve this objective, we assess the levels of MAS for 60 elite male volleyball players (ages 22 to 25 years with +5 years' experience in elite championships). Tested by volleyball Alberta tests and standing triple jump and T agility tests to estimate the effect of coordination abilities on players body adaptations to recover from the high-intensity and fatiguing actions as a result of levels of endurance. Results: Backup on statistics applied, centred on MAS levels as protocol. We confirmed that a high-level of MAS enhanced skills fitness. Admitted by the inverse correlation between the levels of MAS in compares with speed, power and explosive abilities. In the benefits of players with elevated levels of MAS as a beneficial condition to improve skills fitness (Serve-attack/Spike/Block) among volleyball players. Conclusion: our protocol support the development of MAS at 4 + (m/s). Agreed as minimal components of physical condition allied to neuromuscular system adaptations, which permit players not only to improve their speed and power components. But also their ability to recover from high-intensity and fatiguing actions.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.031
Threshold uncertainty score0.998

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)0.0030.000

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 head0.048
GPT teacher head0.340
Teacher spread0.292 · 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.

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

Citations2
Published2019
Admission routes1
Has abstractyes

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