Ways of settling a judo fight at consecutive stages of sports competitions
Bibliographic record
Abstract
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.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".