Kinematic Descriptors of the Running Gait of Field Trial Labrador Retrievers Compared to the Racing Greyhound
Bibliographic record
Abstract
Introduction: The primary objectives of the study were to document and to compare kinematic data of the Field Trial Labrador (FTL) Retriever, during a simulated retrieve, to the Racing Greyhound (RGH). The hypotheses were that FTL will have similar kinematic data to RGHs with the exception that the FTL will be slower and that their stride time will correlate to velocity. Materials and Methods: Six highly competitive FTL Retrievers were filmed retrieving similar to an actual trial. A camera (155 fps) was perpendicular to a 114 m retrieving path. The nose and each paw in the videos were digitized using Kinovea (v0.8.15). The FTL data (velocity, stride time, stride length) were described and compared with previously published RGH data. Descriptive statistics and Pearson Correlation Coefficients (significant at p < 0.05) were performed. Results: The retrieve means were Velocity - 14.11 m/s; Stride Length - 3.22 m; Stride Time - 0.228 second Stride length was the only parameter that was significantly correlated with velocity (R^2=0.83, p = 0.011). Discussion/Conclusion: The FTL stride length was associated with speed where for the RGH it was Stride Time. The FTL (14.11 m/s) wasn’t as fast as RGH (15.83 m/s). FTL Stride Times were 0.228 second RGH were 0.22 second The Stride Length of the FTL (3.22 m) was less than the RGH (5.21 m). The weights of FTL (55# - 80#) and RGH (55# - 88#) are not that different. These findings suggests that power output of the muscles plays a role in the faster speed of the RGH when compared with the FTL. 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 from all over the world, was used. 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.003 | 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".