Bibliographic record
Abstract
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...
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".