Bibliographic record
Abstract
Technical evaluation in the sport of figure skating is characterized by a subjective marking system. Figure skating judges are responsible for quickly and accurately discerning the quality of technical elements as well as assigning a score to the overall aesthetic appearance of a performance. Traditionally, overall placement marks are assigned for the entire performance; however, the landing of a jump is widely acknowledged as one of the most critical elements of a skater's program. Therefore, our aims were to identify the biomechanical variables that contribute to technical success in executing landings and to establish whether landings rated as biomechanically optimal are also awarded high technical merit scores by judges. Ten nationally ranked competitive figure skaters were asked to execute on-ice, double and triple revolution jumps and to try to land the jumps void of technical faults within a calibrated space. Data were collected at 60 Hz using standard three-dimensional videography. Data reduction was done using the APAS system (Ariel Dynamics Inc). Concurrently, videotapes were viewed and evaluated by 42 accredited judges to determine the perceived technical quality of the landing performances. Judges were asked to evaluate the landing phase of each jump against a landing criteria document. A comparative criteria model was developed to facilitate an assessment of excellence in landing performances through both empirical and subjective analyses. Results of these analyses were twofold: a biomechanical profile of on-ice landings was obtained, and on-ice jump landing strategies rated by empirical evaluations were in agreement with judge's perceptions of the same performances.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.033 | 0.011 |
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".