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Record W2795351305 · doi:10.29359/bjhpa.10.1.03

Ways of settling a judo fight at consecutive stages of sports competitions

2018· article· en· W2795351305 on OpenAlexaboutno aff
Marek Adam, Beata Wolska, Sergey Tabakov

Bibliographic record

VenueBaltic Journal of Health and Physical Activity · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicMartial Arts: Techniques, Psychology, and Education
Canadian institutionsnot available
Fundersnot available
KeywordsVictoryMartial artsAthletesCompetition (biology)Competitor analysisPsychologyAdversaryQuarter (Canadian coin)CounterattackOffensiveAdvertisingApplied psychologyPolitical scienceOperations researchComputer securityPhysical therapyEngineeringComputer scienceBusinessLawMarketingHistoryMedicineVisual artsArt

Abstract

fetched live from OpenAlex

Background: At successive stages of sports competition, judo contestants aim at achieving the best sports result. Each fight can be resolved by an effectively executed attack before the regular time (ippon or double waza-ari) or by a better score for efficient attacks (waza-ari or yuko). The victory can also be achieved for penalties awarded to the opponent (shido) or when he/she is disqualified for infractions of the sports rules of the International Judo Federation. Material/Methods: The research material was based on the analysis of bouts fought by male competitors during the World Championships in 2014 and 2015. Results: The results of the conducted analysis allow concluding that at subsequent stages of sports competition the number of fights decided by efficiently executed techniques evaluated as ippon or double waza-ari decreased, just as the number of fights won through the opponent’s disqualification. On the other hand, the number of decisions based on a better score for efficient attacks and the number of fights in which athletes won due to penalties given to their opponents increased. Conclusions: At all the analysed stages of competitions, one quarter of the bouts was decided by penalties which were a consequence of committed infractions.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.458
Threshold uncertainty score0.216

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.064
GPT teacher head0.394
Teacher spread0.329 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations4
Published2018
Admission routes1
Has abstractyes

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Same venueBaltic Journal of Health and Physical ActivitySame topicMartial Arts: Techniques, Psychology, and EducationFrench-language works237,207