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Record W4394267861 · doi:10.6084/m9.figshare.19915199

CORE STRENGTHENING IMPACT ON SOCCER TRAINING OF HIGH SCHOOL PLAYERS

2022· dataset· en· W4394267861 on OpenAlexaff
Bo Wang

Bibliographic record

VenueFigshare · 2022
Typedataset
Languageen
FieldHealth Professions
TopicSports and Physical Education Research
Canadian institutionsVector InstituteUniversity of TorontoUniversity Health NetworkCanadian Institute for Advanced Research
Fundersnot available
KeywordsTraining (meteorology)Core (optical fiber)Mathematics educationPsychologyComputer scienceMedical educationAeronauticsEngineeringGeographyMedicineMeteorologyTelecommunications

Abstract

fetched live from OpenAlex

ABSTRACT Introduction The strengthening of the CORE is one of the essential methods for physical conditioning on elite soccer players, but there are no analyses on the impact of this method on young players. Objective Analyze the strength training impact on the CORE in high school soccer players. Methods This article uses mathematical statistics to study the application of strengthening of the CORE in soccer training for athletes. Based on these results, the role and influence of CORE strengthening training on skills in collegiate soccer training are analyzed. Results After implementing CORE strengthening, both athletes’ fitness indicators and soccer skills were significantly improved. Conclusion Strengthening the CORE can improve players’ stability and balance and contribute to greater effectiveness in physical training. Evidence Level II; Therapeutic Studies - Investigating the result.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.028
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.072
Threshold uncertainty score0.239

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.028
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.004
Bibliometrics0.0040.007
Science and technology studies0.0010.000
Scholarly communication0.0020.001
Open science0.0030.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0720.012

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 head0.361
GPT teacher head0.537
Teacher spread0.176 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreDataset

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

Citations0
Published2022
Admission routes1
Has abstractyes

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