Distribution of power across the hind limb joints in Labrador Retrievers and Greyhounds
Bibliographic record
Abstract
OBJECTIVE: To quantify angular excursions; net joint moments; and powers across the stifle, tarsal, and metatarsophalangeal (MTP) joints in Labrador Retrievers and Greyhounds and investigate differences in joint mechanics between these 2 breeds of dogs. ANIMALS: 12 clinically normal dogs (6 Greyhounds and 6 Labrador Retrievers) with no history of hind limb lameness. PROCEDURE: Small retroreflective markers were applied to the skin over the pelvic limb joints, and a 4-camera kinematic system captured data at 200 Hz in tandem with force platform data while the dogs trotted on a runway. Breed-specific morphometric data were combined with kinematic and force data in an inverse-dynamics solution for stance-phase net joint moments and powers at the stifle, tarsal, and MTP joints. RESULTS: There were gross differences in kinematic patterns between Greyhounds and Labradors. At the stifle and tarsal joints, moment and power patterns were similar in shape, but amplitudes were larger for the Greyhounds. The MTP joint was a net absorber of energy, and this was greater in the Greyhounds. Greyhounds had a positive phase across the stifle, tarsal, and MTP joints at the end of stance for an active push-off, whereas for the Labrador Retrievers, the only positive phase was across the tarsus, and this was small, compared with values for the Greyhounds. CONCLUSIONS AND CLINICAL RELEVANCE: Gross differences in pelvic limb mechanics are evident between Greyhounds and Labrador Retrievers. Joint kinetics in specific dogs should be compared against breed-specific patterns.
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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 | 0.000 |
| 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) | 0.002 | 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".