Distribution of vertical forces in the pads of Greyhounds and Labrador Retrievers during walking
Bibliographic record
Abstract
OBJECTIVE: To document peak vertical force (PVF) and vertical impulse (VI) in the pads of Greyhounds and Labrador Retrievers. ANIMALS: 8 Greyhounds and 8 Labrador Retrievers. PROCEDURE: Velocity and acceleration were restricted to ranges of 0.9 to 1.1 m/s and -0.1 to 0.1 m/s2, respectively. The PVF and VI measurements were collected from digital pad (DP)-2, -3, -4, and -5 and the metacarpal pad (McP) or metatarsal pad (MtP) of each limb in each dog. RESULTS: We found no significant differences between the left and right forelimbs or hind limbs for any pad in either breed. Vertical forces in the forelimb were always greater than those in the hind limb. The PVF in the forelimbs of Greyhounds was greatest in DP-3, -4, and -5 and DP-3, DP-4, and the MtP in the hind limbs. The VI in Greyhound forelimbs was greatest in DP-3, -4, and -5 but greatest in DP-4 in the hind limbs. The PVF in the forelimbs of Labrador Retrievers was greatest in the McP, whereas in the hind limbs it was greatest in DP-4. The VI in Labrador Retriever forelimbs was greatest in DP-3, DP-4, and the McP but greatest in DP-3 and -4 in the hind limbs. Significant differences were detected in load distribution between the breeds. CONCLUSIONS AND CLINICAL RELEVANCE: This study confirms that DP-3 and DP-4 are major weight-bearing pads in dogs. However, loads were fairly evenly distributed, and DP-5 and the McP or MtP bear a substantial amount of load in both breeds.
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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 | 0.000 |
| 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".