Assessment of energy expenditure in elite jockeys during simulated race riding and a working day: implications for making weight
Bibliographic record
Abstract
Professional jockeys are unique amongst weight-making athletes in that they are required to make weight on a daily basis by often using potentially hazardous methods, such as food deprivation, dehydration, and forced vomiting. To allow the prescription of accurate energy intake (EI), it is essential to understand the energy requirements of jockeys; however, these data are currently not known. Therefore, we measured the energy expenditure (EE) of professional jockeys during a simulated race ride and for a working day (nonracing) that involved typical stable duties. The accuracy of 2 portable lightweight devices, the Polar RS400 commercial heart rate monitor (CHRM) and the Actiheart monitor (AH) were initially assessed during 30 min of exercise compared with respiratory gas analysis (GA) (n = 9). No significant difference was observed (p > 0.05) and 95% limits of agreement analysis (LoA) showed that CHRM was more closely related to GA (bias: -0.015; LoA: -0.049, +0.019 MJ) than AH (bias: -0.007; LoA: -0.073, +0.059 MJ). A laboratory-based 2-mile (3.2-km) racing protocol was created and EE was assessed using CHRM, GA, and AH. We report that a typical race expends 0.18 (SD ±0.03) MJ. Finally, in a separate group of jockeys (n = 8), 24-h EE was assessed using CHRM. The mean (±SD) EE for a typical day was 11.26 (±1.49) MJ. Additionally, we measured EI using 7-day self-reporting food record diaries. Mean EI was 7.24 (±0.92) MJ, largely consumed as 2 main meals. These data provide a platform to implement dietary strategies that create appropriate weight-loss targets and therefore improve the physical and mental well-being of professional jockeys.
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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 | 0.002 |
| 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) | 0.000 | 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".