Compensatory load redistribution in Labrador retrievers when carrying different weights – a non-randomized prospective trial
Bibliographic record
Abstract
BACKGROUND: Retrievers are dogs particularly bred to retrieve birds or other small game, for the retrieval, the dogs are typically sent to the place where the shot game has fallen or to search the field for the wounded but still live game in order to return them to the hunter as quickly as possible. Examples of game animals are pheasants, mallard ducks and rabbits. For training, dummies with a variety of weights are used to simulate the retrieval of various types of game. The aim of this non-randomized prospective study was to investigate if peak vertical force, vertical impulse and paw pressure contact area are increased in the forelimbs when carrying different weights, and if the symmetrical weight distribution between contralateral limb pairs is disturbed. Ten actively working Labrador retrievers were walked over a pressure plate with or without carrying 0.5, 2.0 and 4.0 kg dummies. The aim of this study was to determine if vertical ground reaction forces and paw pressure contact area are increased in the forelimbs when carrying different weights, and if symmetrical weight distribution is disturbed between contralateral limb pairs. RESULTS: Peak vertical force and vertical impulse were significantly increased in the forelimbs and decreased in the hindlimbs in all weight carrying conditions. CONCLUSIONS: These results demonstrate the significant effects of carrying weight in the mouth on the ground reaction forces, which likely produce additional stress on the forelimb joints. Carry of game or a dummy is likely to alter the forelimb load distribution.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".