Force Platform Analysis in Clinically Healthy Rottweilers: Comparison with Labrador Retrievers
Bibliographic record
Abstract
OBJECTIVE: To (1) report ground reaction forces for healthy Rottweilers at a trot and (2) compare force platform data with values obtained for healthy Labradors. STUDY DESIGN: Prospective, clinical study. ANIMALS: Adult Rottweilers (n=9) and Labrador Retrievers (12) without orthopedic abnormalities. METHODS: Dogs were trotted over a force platform at controlled speed and acceleration. Peak vertical and craniocaudal forces, associated impulses, stance time, rising, and falling slopes were analyzed and forces, impulses, and slopes were expressed as percentages of body weight. The effects of weight and anatomic measurements on force platform values were re-evaluated with analysis of covariance (ANCOVA). RESULTS: In Rottweilers, peak vertical forces in thoracic limbs were significantly lower and vertical impulses in thoracic and pelvic limbs were significantly higher than in Labradors. Rising and falling slopes in thoracic and pelvic limbs were significantly smaller in Rottweilers. Body weight and anatomic measurements were significantly larger in Rottweilers. After removing the effect of relative velocity, functional limb length, and body weight by using ANCOVA, there were no significant differences between breeds. CONCLUSIONS: Ground reaction forces were significantly different between Rottweilers and Labradors when using standard methods of normalization. Based on ANCOVA differences were attributable to difference in conformation and body weight between breeds. CLINICAL RELEVANCE: Conformation and body weight have a significant influence on force platform values and this may cause bias when study results are compared.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.001 | 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 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".