The biomechanics of working dog locomotion II: Loaded trotting
Bibliographic record
Abstract
There is a growing need for working dogs to carry equipment to facilitate or enhance the completion of tasks, and carrying these wearable devices often involves bearing substantial loads on or around their spines. However, the potential locomotory, physiological and/or behavioural effects of the shape and/or weight of these devices on working dog performance are unknown. This work aimed to investigate the absolute and relative impact of carrying various shapes and magnitude of load on functional performance in working dogs, including Labrador retrievers, shepherd breeds and spaniel breeds. Functional parameters were analysed from each dog during trotting whilst carrying loads that included bags (10% body mass) and a cylindrical tube (5% or 10% body mass). The impact of load type and mass appeared to be breed-specific, with shepherd breeds incurring the greatest impacts in terms of altered spatiotemporal parameters relative to unloaded trotting. Spaniel breeds showed less impact from both load conditions in spatiotemporal gait parameters, but larger differences in joint kinematics and moments relative to the unloaded condition. These data point to a lack of generalisability in the responses to load carrying, even between breeds of broadly similar body shapes, but tentatively suggest that spaniel breeds may perform better during trotting load-carrying tasks, although they may do so at a possible cost of increased muscle forces and joint loads. Further work incorporating measures of energetic expenditure and fatigue is needed to test these hypotheses.
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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 | 0.001 |
| 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".