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Record W4394229807 · doi:10.6084/m9.figshare.21556990

IMPROVING EXPLOSIVE BODY CAPACITY IN FEMALE SHORT TRACK SPEED SKATERS

2022· dataset· en· W4394229807 on OpenAlexaff
Jianjun Li, Xinchao Ge, Yang Liu

Bibliographic record

VenueFigshare · 2022
Typedataset
Languageen
FieldMedicine
TopicCardiovascular Effects of Exercise
Canadian institutionsMcMaster UniversityUniversity of British Columbia
Fundersnot available
KeywordsSpeed skatingExplosive materialTrack (disk drive)Computer scienceSimulationHistoryOperating system

Abstract

fetched live from OpenAlex

ABSTRACT Introduction The peculiar characteristics of short track speed skating should be integrated into the psychology of competitions; it is considered that elite athletes engaged in this particular sport should have a healthy psychic condition. Objective Investigate the explosive power of female speed skaters in short track speed skating. Methods 10 key athletes from the national short track speed skating team were selected, and explosive power was tested by T-test, hexagonal test, and pro sensitivity test. Data analysis was performed using an independent sample t-test, differences in the results of related test indicators between groups were analyzed, and repeated measures analysis of variance was used. Results During the explosive kick phase, knee extension speed increased linearly from 210°/S to 600°/S, and hip extension speed increased linearly from 200°/S to 400°/S. Conclusion The development of muscle group strength and explosive power during training often shows differences in the degree of contraction, which is related to the arrangement of training methods. Level of evidence II; Therapeutic studies - investigation of treatment outcomes.

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)0.002
metaresearch head score (Gemma)0.014
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 score0.053
Threshold uncertainty score0.179

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.014
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.003
Bibliometrics0.0020.003
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0530.008

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 head0.056
GPT teacher head0.276
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

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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