Verification of skin‐based markers for 3‐dimensional kinematic analysis of the equine tarsal joint
Bibliographic record
Abstract
REASONS FOR PERFORMING STUDY: Kinematic studies are usually based on tracking markers attached to the skin. However, complex joints, such as the tarsal joint, function in 3-dimensions (3D), and have therefore necessitated application of the invasive bone pin technique, limiting kinematic studies to the research laboratory. This study investigates the feasibility of using skin-based markers for 3D analysis of tarsal joint motion. HYPOTHESIS: Three-dimensional motions of the tarsal joint can be measured with an acceptable degree of accuracy using skin markers. METHODS: Retroreflective markers were attached over the tibial and metatarsal segments. Markers were tracked automatically at trot. Three-dimensional skin correction algorithms were used for correction of skin displacement, and 3D motions derived from the corrected (CSD) and uncorrected (USD) skin displacement were compared with data from a previous study in which those motions were described using bone-fixed markers (BFM) by correlation, root mean square errors (RMS) and shape agreement (SA) of the curves. RESULTS: The RMS of BFM and CSD were smaller than those of BFM and USD for all motions. The correlation coefficients of BFM and CSD were higher than those of BFM and USD. SA was good or fair for all motions except internal/external rotation and medial/lateral translation. CONCLUSIONS AND POTENTIAL RELEVANCE: With appropriate correction for skin movement relative to skeletal landmarks, skin markers can identify tarsal 3D motions for flexion/extension, abduction/adduction, cranial/caudal translation, and proximal/distal translation, allowing analysis and comparison of information between horses during swing and stance phases.
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 | 0.009 | 0.030 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".