FIABILIDAD DEL SISTEMA DE PUNTUACIÓN EN LA COMPETICIÓN DE HALFPIPE - SNOWBOARDING DE LOS JUEGOS OLÍMPICOS DE VANCOUVER 2010 [Scoring system reliabilty in Vancouver 2010 Winter Olympic Games Halfpipe-Snowboarding Competition]
Bibliographic record
Abstract
Snowboarding-Halfpipe is a high performance sport discipline which is included in Winter Olympic Games program. Competition system is based on the observation of riders' technical performance, for which judges take as reference the official manual that collects principal aspects of exercises scoring. Given the inherent characteristics of this sport, in which performance is based on the observation ability of judges, training and experience of this judges is crucial in order to turn the observation into a measurement as objective as possible. Accordingly, this article aims to quantify reliability indices between judges' scores as well as their relationship to the gender, the presence of falls and the number of hops. The study sample comprised all the exercises developed during the final two rounds of the Vancouver 2010 Olympic Winter Games, both male (n=24) and female competition (n=22). Results showed that the reliability between judges is excellent (Round 1: ICC=0,985, αC=0,997, Round 2: ICC=0,991, αC=0,998), regardless of gender, presence of falls or the number of hops.
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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 | 0.018 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".