Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it