Effects of warm-up intensity on kinetics of oxygen consumption and carbon dioxide production during high-intensity exercise in horses
Bibliographic record
Abstract
OBJECTIVE: To compare effects of low and high intensity warm-up exercise on oxygen consumption (VO2) and carbon dioxide production (VCO2) in horses. ANIMALS: 6 moderately conditioned adult Standard-breds. PROCEDURES: Horses ran for 2 minutes at 115% of maximum oxygen consumption (VO2max), 5 minutes after each of the following periods: no warm-up (NoWU); 10 minutes at 50% of VO2max (LoWU); or 7 minutes at 50% VO2max followed by 45-second intervals at 80, 90, and 100% VO2max (HiWU). Oxygen consumption and VCO2 were measured during exercise, and kinetics of VO2 and VCO2 were calculated. Accumulated O2 deficit was also calculated. RESULTS: For both warm-up trials, the time constant for the rapid exponential increase in VO2 was 30% lower than for NoWU. Similarly, the rate of increase in VCO2 was 23% faster in LoWU and HiWU than in NoWU. Peak values for VO2 achieved during the high-speed test were not significantly different among trials (LoWU, 150.2 +/- 3.2 ml/kg/min; HiWU, 151.2 +/- 4.2 ml/kg/min; NoWU, 145.1 +/- 4.1 ml/kg/min). However, accumulated O2 deficit (ml of O2 equivalents/kg) was significantly lower during LoWU (65.3 +/- 5.1) and HiWU (63.4 +/- 3.9) than during NoWU (82.1 +/- 7.3). CONCLUSIONS AND CLINICAL RELEVANCE: Both the low- and high-intensity warm-up, completed 5 minutes before the start of high-intensity exercise, accelerated the kinetics of VO2 and VCO2 and decreased accumulated O2 deficit during 2 minutes of intense exertion in horses that were moderately conditioned.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".