Comparison of game skills between Czech, New Zealand and Canadian nation team of women's rugby
Bibliographic record
Abstract
Theme of the work: Comparison of game skills between Czech, New Zealand and Canadian nation team of women's rugby Student: Eva Zdeňková Supervisor: doc. PhDr. Jiří Suchý, Ph.D. Aims: The purpose of bachelor thesis is to found the biggest lack of game skills and the standard situations in Czech national team in comparison with studied teams and judge if skill errors of players are significant for the results of particular matches. Methodology: Firstly we work up assignments. With the help of the created stats we will compare differences in success of particular teams. Also we will ascertain if skill errors of players are significant for the results of matches. Results: There is no significant difference in between the game skills of Czech national team and both New Zealand or Canadian national teams. A slight difference could be seen in; passing, dynamics and continuity of a game. Those differences are not as visible as expected, therefore the more significant differences in between the observed teams, have been suspected to come up from players conditioning. Keywords: Rugby union, game skills, standard situation, game performance, individual error.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 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".