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Record W4403499602 · doi:10.5114/jhk/191699

The Evaluation of the Modified Wave Periodization Model Efficiency on the Example of Young Soccer Players' Sprint Tests

2024· article· en· W4403499602 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueJournal of Human Kinetics · 2024
Typearticle
Languageen
FieldMedicine
TopicSports Performance and Training
Canadian institutionsnot available
Fundersnot available
KeywordsSprintPeriodizationComputer scienceSimulationSoftware engineering

Abstract

fetched live from OpenAlex

The research aimed to evaluate the modified model of wave periodization efficiency in running speed tests conducted among soccer players aged 12 to 16. Participants included prospective players of a leading Polish top league soccer club. The research was carried out from 2018 to 2022 in June (Testing A) and December (Testing B) of each year. The test involved 30-m straight line running with 5-, 10-, and 30-m split time measurements. For this purpose, electronic photocells were used (FITLIGHT, Canada). The six-month training intervention increased the athletes' speed as there was a considerable decrease in the running time over the distance of 5 m (F = 7.86; p < 0.001), 10 m (F = 73.99; p < 0.001) and 30 m (F = 127.55; p < 0.001). Analysis of running performance of young soccer players aged 12-16 showed a significant improvement in speed at distances of 5, 10 and 30 m, confirming training effectiveness based on the wave periodization model. The negative correlation between testing year and performance suggests the influence of biological development on players' speed. The COVID-19 pandemic has impacted training, which was reflected in reduced differences between test scores. Improving initial running technique can contribute to better match results, which emphasizes the need for an individual approach to the physical preparation of players.

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.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.204
Threshold uncertainty score0.137

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 integrity0.0000.000
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 head0.126
GPT teacher head0.347
Teacher spread0.221 · 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