The precision and repeatability of a custom-made pointer device for determination of virtual landmarks in canine three-dimensional kinematics
Bibliographic record
Abstract
Placement of markers on anatomical landmarks represents a large source of error in three-dimensional kinematics. Our objectives were to test the accuracy and precision of a custom-made pointer and compare it to conventional skin markers in dogs. The pointer was first assessed by pointing at the surface of a spherical marker of known dimensions and position in space. Secondly, a point located cranio-distally to the lateral epicondyle was marked in 12 canine elbows with a Steinmann pin and reflective markers. Ability to locate a landmark was compared between the pointer and skin-mounted marker. The distance between experimental and true locations was compared between the two methods. A sphere was mathematically fitted through 29 collected points on the spherical marker. Centre, diameter and volume overlap of the fitted sphere were compared to that of the marker. A 0.729 mm bias was found indicating good accuracy. Residual values were small indicating good precision. The average distance between the true and experimental position of the anatomical landmarks were 9.55 ± 4.20 mm and 9.32 ± 3.28 mm for the pointer and the marker respectively. No significant differences were observed between the two methods. The pointer proved to be accurate and reliable for localizing virtual points and was at least equivalent to skin mounted markers for the detection of anatomical landmarks in the dog. It should prove useful in the localization of anatomical landmarks for kinematic analysis.
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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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| 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) | 0.001 | 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".