The Structure of ladies' figure skating short program in 2010 Olympic Games
Bibliographic record
Abstract
The purpose of this work was to examine senior ladies short program element structure which was skated at the Winter Olympic Games in Vancouver 2010. The study covers all 30 skaters that were qualified for the Games. \nTheory part scratches the surface of demands to become a top athlete and continues with the demands of the program in rule wise and represents the new judging system of figure skating and how it has brought new demands to the sport. The research part is defining structure of ladies short program focusing on tactical side; is there any common laws among top skaters to build programs and are there any structural differences between the champion and the number 30 or are there any differences in timing/placing the elements that succeeded or didn’t were couple of ultimate questions when starting to do this study. \nSkaters 1-15 had a quite the same program structure with only minor differences on elements. They mainly tried to perform the hardest jump elements on a first two elements and then have a small “breathing break” before double axel, the last two spins and step sequence which is quite physical to perform with the new judging system. Most of the mistakes happened among the hardest and most valuable jumps. \nSkaters 16-30 mainly opened with easier jump and then did all the jumps in a row. They did mistakes through the program and also with spins which should be “the easy ones” from element list to perform with good quality. Also the program structure differed quite a lot compared to best 15 skaters. \nThe higher ranked skaters were able not just to perform with good quality harder and more valuable elements, they also planned to do totally different caliber elements than e.g. skaters ranked 20-30. On many element, skaters lost too much points considering the base value and it is reasonable to present the hypothesis to plan the program according to skill level of the skaters’ normal, everyday skill level, not according to what it might take to qualify for free skating at the certain event. By doing this, the chances to qualify for free skating increases because of higher grade of execution (GOE) values that seem to be the key factor to earn highest score from the performance in today’s figure skating.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".