A photogrammetric application in virtual sport training
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
Abstract The paper discusses an application of close range photogrammetry for the development of a virtual training system for rugby football, and the use of the technique for the evaluation of rugby players’ performance. NuView, a stereo‐imaging device, and a digital high‐definition video (HDV) camera were used to capture stereoscopic video footage of players during field training. The left view and the right view were colour‐tinted cyan and red, respectively. The tinted stereo (anaglyph) views were projected onto a white screen, and players were instructed to practise ball‐throwing at the screen. A custom‐built laser device (TAM) measured the accuracy of the virtual throws. In addition, a photogrammetric system was used to track the movement of body segments, for example, the angle of shoulder orientation and the trunk flexion of the thrower. The measurements determined the parameters needed for an accurate throw and these parameters would be used in the training of new players. The study shows statistically significant differences in the values of these parameters between experienced and inexperienced players.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.008 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 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