Comparison of the trotting gaits of Labrador Retrievers and Greyhounds
Bibliographic record
Abstract
OBJECTIVE: To compare the trotting gaits of Labrador Retrievers and Greyhounds to determine whether differences in locomotion are attributable to differences in their manner of moving or to body size and shape differences between these 2 breeds. ANIMALS: 8 healthy 5-month-old Greyhounds and 5 healthy Labrador Retrievers between 6 and 18 months old. PROCEDURE: A series of 4 force platforms was used to record independent ground reaction forces on the forelimbs and hind limbs during trotting. Values of stride parameters were compared between breeds before and after normalization for size differences. Standard values of absolute and normalized stride period and stride length were determined from linear regressions of these parameters on relative (normalized) velocity. Forces were normalized to body weight and compared at the same relative velocity. RESULTS: Greyhounds used fewer, longer strides than the Labrador Retrievers to travel at the same absolute speed. After normalization for body size differences, most measurable differences between breeds were eliminated. Subtle differences that did persist related to proportion of the stride that the forefoot was in contact with the ground, timing of initial hind foot contact relative to initial forefoot contact, and distribution of vertical force between the forelimbs and hind limbs. CONCLUSIONS AND CLINICAL RELEVANCE: Results suggest that apparent differences in the trotting gait between Labrador Retrievers and Greyhounds are mainly attributable to differences in size, and that dogs of these 2 breeds move in a dynamically similar manner at the trot.
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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) | 0.001 | 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".