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Record W7135735273

Conditioning preparation for U15 players at the Czech Hockey Academies

2023· dissertation· cs· W7135735273 on OpenAlexaboutno aff
Matyáš Rendla

Bibliographic record

VenueDigital Repository (National Repository of Grey Literature) · 2023
Typedissertation
Languagecs
FieldPsychology
TopicSport Psychology and Performance
Canadian institutionsnot available
Fundersnot available
KeywordsCzechIce hockeyEliteConditioningWork (physics)
DOInot available

Abstract

fetched live from OpenAlex

Title: The conditioning off -ice preparation of ice hockey players up the age of 15 at Czech Hockey academy Objectives: The aim of this study was to investigate and to compare the conditioning off- ice preparation of Czech elite ice hockey players up to their age of 15 with the conditioning programs of elite players from Scandinavian and overseas countries. Furthermore, I aimed to find out how different development systems work and what advantages and disadvantages they have. Methods: To gather data, I created an anonymous non-standardized questionnaire in Czech and English, targeting adult elite ice hockey players. The players were from Sweden, Canada, and the Czech Republic. With a sufficient number of respondents, I divided Czech players into younger and older generations. The questionnaire was distributed through my friends and colleagues, primarily those involved in ice hockey. Respondents answered 11 questions regarding participation in other sports, off-ice training, and PE content until the age of 15. Results: The findings of my research revealed that younger generation of Czech players engaged in another sport up to the age of 11.2(on average), which is more than 2 years less than their Canadian or Swedish counterparts. A similar difference was observed in the duration of supplementary...

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.454
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0020.001
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 head0.019
GPT teacher head0.337
Teacher spread0.318 · 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 designNot applicable
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

Citations0
Published2023
Admission routes1
Has abstractyes

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