Capturing Kinematics on Competitive Trial Labradors in the Field
Bibliographic record
Abstract
Introduction: While much is known about the gaits and kinetics of Labrador Retrievers on treadmills and in laboratories, little is documented about their running kinematics in a field situation. Our goals were to develop a feasible method to capture movement parameters of field trial Labrador Retrievers enroute to the retrieve and to evaluate fatigue over multiple runs. Materials and Methods: Seventeen healthy dogs were filmed, but only the runs of six dogs were digitized consisting of 2 intact males, 4 intact females, weighing 22–31.5kg and aged 1.7–7.1years. The test simulated field trial (FT) training. Three successive runs were performed with the run out on the first and third runs filmed. An IDS camera (155 fps) was positioned perpendicular to the retrieving path. The videos were then digitized and analyzed using Kinovea (v0.8.15). Descriptive statistics of the kinematic data of the dogs were performed (significant at p < 0.05) and relationships between kinematic variables between runs were assessed. Results: There were no statistically significant differences in velocity, front or rear stance or flight phases or stride length between the analyzed runs. Discussion/Conclusion: This portable set up provided a way to capture temporospatial parameters and methodology to evaluate multiple runs in the field, although fatigue did not affect parameters. Future captures could include longer distances as well as multiple retrieve sessions. Further evaluation of each stride phase could identify factors affecting movement as well as document variations in abnormal patients. Continued kinematic analysis can provide answers about conditioning and rest periods to prevent fatigue while minimizing impactful movement. Acknowledgment: There was no proprietary interest for this project. Equipment and funds for this project were provided by the investigators. Kinovea, a free 2D motion analysis software under GPLv2 license, created in 2009 via the non-profit collaboration of several researchers, athletes, coaches and programmers. Publication History Article published online: 09 April 2024 © 2024. The Author(s). This is an open access article published by Thieme under the terms of the Creative Commons Attribution License, permitting unrestricted use, distribution, and reproduction so long as the original work is properly cited. (https://creativecommons.org/licenses/by/4.0/) Georg Thieme Verlag KG Rüdigerstraße 14, 70469 Stuttgart, Germany
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".