Motion database of disguised and non-disguised team handball penalty throws by novice and expert performers
Bibliographic record
Abstract
This article describes the motion database for a large sample ( n = 2400) of 7-m penalty throws in team handball that includes 1600 disguised throws. Throws were performed by both novice ( n = 5) and expert ( n = 5) penalty takers. The article reports the methods and materials used to capture the motion data. The database itself is accessible for download via JLU Web Server and provides all raw files in a three-dimensional motion data format (.c3d). Additional information is given on the marker placement of the penalty taker, goalkeeper, and ball together with details on the skill level and/or playing history of the expert group. The database was first used by Helm et al. (2017) [1] to investigate the kinematic patterns of disguised movements. Results of this analysis are reported and discussed in their article “Kinematic patterns underlying disguised movements: Spatial and temporal dissimilarity compared to genuine movement patterns” ( doi:10.1016/j.humov.2017.05.010 ) [1] .
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.011 | 0.006 |
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".