Comparison of physiological demands in Warmblood show jumping horses over a standardized 1.10 m jumping course versus a standardized exercise test on a track
Bibliographic record
Abstract
BACKGROUND: A greater understanding of exercise physiology and biochemistry is required for the sport horse disciplines, including show jumping. Conditioning of horses for show jumping is empirical because they are primarily trained on flat ground, however the equivalent workload between jumping and flat work is currently unknown. The objectives of the study were therefore to compare the physiological demands of Warmblood show jumpers over a standardized 1.10 m course vs a 600 m standardized incremental exercise test on flat ground, and to report reference field test values for competitive show jumping horses. In this prospective field study, 21 healthy, actively competing Warmblood show jumping horses were assessed to determine physiological variables after a standardized jumping course at 6.4 m/s (average speed) and track standardized incremental exercise test at 5 m/s, 8 m/s and 11 m/s. Heart rate, velocity, blood lactate, blood pH, pCO2, bicarbonate, PCV and TP concentrations were recorded. V200, V170 and VLa4 were calculated. Parametric statistics were performed on analysis of all 21 horses' variables. RESULTS: Contrary to exercise at 5 m/s and 11 m/s, cantering at 8 m/s did not induce any significant difference in blood lactate, mean heart rate or mean venous blood pH compared to after completion of the jumping course. CONCLUSIONS: Jumping a 1.10 m course demands a statistically similar workload to cantering around a flat track at 8 m/s. This study will help to test fitness and design conditioning programs for Warmblood show jumping horses.
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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 | 0.000 | 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".