Soft Tissue Artifact in Canine Kinematic Gait Analysis
Bibliographic record
Abstract
OBJECTIVE: To investigate, noninvasively, the soft tissue artifact (STA) in canine kinematic gait analysis. STUDY DESIGN: Experimental study. ANIMALS: Labrador retrievers (n = 4). METHODS: Kinematic study: Reflective markers were glued to the skin over bony landmarks, with the distance between 2 markers representing the length of the underlying scapula, humerus, ulna, femur, and crus. The distance between these markers (marker distance [MD]) was measured with infrared cameras while the dogs stood still or walked on a treadmill. Fluoroscopy study: Radiopaque markers were glued on the skin over the spinous process of the L6 vertebra and the stifle to allow fluoroscopic observation of the markers and underlying skeletal segments while the dogs walked on the treadmill. The position of the markers was compared with the position of the underlying skeletal segments during different phases of the step cycle. RESULTS: Kinematic study: Significant differences were found between MD during standing and walking for all bones investigated. Mean percentage differences in MD ranged from -18% to +6%. Fluoroscopy study: Significant displacements relative to the bony landmarks were found ranging from 0.4 to 1.2 cm. CONCLUSIONS: Analysis of the motion of skeletal structures with the use of markers attached to the skin showed that the skin moves relative to underlying skeletal structures. When working with a 3-D motion-capture system using skin markers, researchers should be aware that the STA could significantly influence their results.
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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 | 0.002 | 0.005 |
| 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 | 0.001 | 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".