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Record W7033191130

Porovnání herních činností českého, novozélandského a kanadského národního týmu ženského ragby

2017· dissertation· en· W7033191130 on OpenAlexaboutno aff

Bibliographic record

VenueDigital Repository (National Repository of Grey Literature) · 2017
Typedissertation
Languageen
FieldSocial Sciences
TopicLegal case studies and regulations
Canadian institutionsnot available
Fundersnot available
KeywordsNucleofectionArticular cartilage damageDiafiltrationTSG101HyporeflexiaGestational period
DOInot available

Abstract

fetched live from OpenAlex

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.

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, Scholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.545
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 studies0.0040.001
Scholarly communication0.0030.002
Open science0.0010.000
Research integrity0.0010.001
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 head0.011
GPT teacher head0.290
Teacher spread0.279 · 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
GenreOther

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
Published2017
Admission routes1
Has abstractyes

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