Repeatability of force signals in aerial circus straps
Bibliographic record
Abstract
The aim of this study is to develop a method for assessing movement variability of circus acrobats. An analysis of the repeatability of force signals is used to quantify variability. Six students from the National Circus School of Montréal performed 5–10 trials of an acrobatic movement called dislock in aerial circus straps while tension force was measured at the hanging point of the aerial apparatus. The repeatability of force signals was calculated with three statistical methods: time-averaged standard deviation, intraclass correlation and root mean square error. These methods were compared with the ratings of a circus coach who ranked each acrobat’s trial with regard to the movement variability. The standard deviation and the intraclass correlation methods are commonly used to quantify the agreement between measurements in biomechanics, while the root mean square error method is regularly employed to quantify the agreement between measurements and a model. All participants performed the movement with little variability (intraclass correlation ⩾ 0.8). The results of the three methods were in good agreement with the coach’s assessment. The root mean square error method, in particular, showed perfect agreement and is therefore considered the best measure of repeatability. In the future, the proposed method could be used by coaches or artists training alone, allowing a new form of feedback.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| 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) | 0.001 | 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".