ACCURATE VELOCITY ASSESSMENT OF A ROWING SKIFF USING KINEMATIC GPS
Bibliographic record
Abstract
Un des problemes qui pose le plus de defis dans le sport de l'aviron est de determiner comment la velocite d'une yole reagit au pouvoir combine et a la technique d'aviron de l'equipage. Les techniques actuelles, comme celles de l'ergometre sont inexactes ou encombrantes et ne refletent pas avec exactitude les changements subtils de la technique que l'equipe d'aviron pourrait faire sur l'eau. La question demeure, est-ce que de telles reponses sont bonnes ou mauvaises, grandes ou petites. Si la velocite d'une yole peut etre determinee avec une exactitude suffisante, cette information pourra aider la selection de l'equipage et leur entrainement en fournissant des resultats nets tant pour l'entraineur que pour les athletes. Dans ce projet, la technologie du systeme mondial de localisation (GPS) jumelee a des essais dynamiques ont ete utilises pour illustrer que les recepteurs GPS sont une option viable pour determiner la velocite et l'acceleration d'une yole de rameurs. Deux essais ont ete executes pour mesurer la cinematique des equipages de six rameurs et de huit rameurs en mesurant la velocite et l'acceleration de la yole et l'effet de stabilite de la yole sur la velocite et l'acceleration mesurees.
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.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".