Workload of horses on a water treadmill: effect of speed and water height on oxygen consumption and cardiorespiratory parameters
Bibliographic record
Abstract
Despite the use of water treadmills (WT) in conditioning horses, the intensity of WT exercise has not been well documented. The workload on a WT is a function of water height and treadmill speed. Therefore, the purpose of this study was to determine the effects of these factors on workload during WT exercise. Fifteen client-owned Quarter Horses were used in a randomized, controlled study. Three belt speeds and three water heights (mid cannon, carpus and stifle), along with the control condition (dry treadmill, all three speeds), were tested. Measured outcomes were oxygen consumption (V̇O 2 ), ventilation (respiratory frequency, tidal volume (V T )), heart rate (HR), and blood lactate. An ergospirometry system was used to measure V̇O 2 and ventilation. Linear mixed effects models were used to examine the effects of presence or absence of water, water height and speed (as fixed effects) on measured outcomes. Water height and its interaction with speed had a significant effect on V̇O 2 , V T and HR, all peaking at the highest water level and speed (stifle at 1.39 m/s, median V̇O 2 = 16.70 ml/(kg.min), V T = 6 L, HR = 69 bpm). Respiratory frequency peaked with water at the carpus at 1.39 m/s (median 49 breaths/min). For a given water height, the small increments in speed did not affect the measured outcomes. Post-exercise blood lactate concentration did not change. Varying water height and speed affects the workload associated with WT exercise. The conditions utilized in this study were associated with low intensity exercise. Water height had a greater impact on exercise intensity than speed.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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".