Assessment of high-intensity over-ground conditioning and simulated racing on aerobic and anaerobic capacities in racehorses
Bibliographic record
Abstract
A prospective, randomised study assessed the impact of high-intensity racetrack conditioning on aerobic and anaerobic capacities in seasoned Thoroughbred racehorses. The effect of 10 weeks race conditioning and two simulated races on V̇O2max and maximum accumulated oxygen deficit (MAOD) were evaluated. An incremental treadmill test to determine V̇O2max, followed by three supramaximal runs to fatigue (at speeds (V105%, V115%, V125%) corresponding to oxygen requirements 105%, 115% and 125% of V̇O2max, in randomised order) were performed at each timepoint (T1 [pre-conditioning] and T2 [post-conditioning]). Prior to T1, racehorses were briefly de-trained for four-six weeks and given low-level treadmill conditioning to prepare them for the more strenuous race conditioning after T1. Paired variables between T1 and T2 were analysed using a paired t-test. A 2-way RM ANOVA compared variables with >1 measurement. Speed at V̇O2max (P=0.04) and V̇O2max (P=0.01) increased with conditioning. Calculated speeds for the supramaximal runs increased for V105% (P=0.02) and V115% (P=0.03) but not for V125% (P=0.08). There was no conditioning effect on time to fatigue (P=0.34), although it was different between all intensities (2.8, 2.2 and 1.4 mins at V105%, V115% and V125% respectively at T2). O2 demand increased with conditioning (P=0.02) for each supramaximal intensity. On average, horses’ aerobic capacity improved 4.43% after conditioning. MAOD was unchanged with conditioning (P=0.25) and unaffected by exercise intensity. Fit racehorses that have undergone repeated intensive training programs, experience smaller, incremental improvement than completely unfit horses. The anaerobic capacity of previously trained racehorses is relatively stable, despite brief periods of de-training.
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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) | 0.001 | 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 | 0.001 | 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".