Using eye temperature and heart rate for stress assessment in young horses competing in jumping competitions and its possible influence on sport performance
Bibliographic record
Abstract
The aims of this study were, first, to evaluate eye temperature (ET) with infrared thermography and heart rate (HR) to measure stress in horses during show jumping competitions and their relationship with competition results, and second, to evaluate the influence of different extrinsic and intrinsic factors of the horse on the stress measurements analysed. One hundred and seventy-three Spanish Sport Horses were analysed for ET and HR, and these measurements were taken 3 h before the competition, just after and 3 h after it. Two interval measurements were also assessed for each parameter. Positive significant correlations were found between ET and HR, measured before (r=0.23), just after competition (r=0.28) and for the later interval (r=0.26), whereas negative correlations with competition results were found only for ET when measured just after competing (r=-0.25). Two intrinsic factors (genetic line and age) and no extrinsic factors showed significant differences for ET, whereas one intrinsic factor (age) and two extrinsic factors (journey duration and number of training hours) showed significant differences for HR. The marginal means showed significantly higher ET values for the Anglo-Arab genetic line and for 5-year-old animals. HR values were significantly higher for 4-year-old animals, for horses which had travelled 4 to 6 h and for horses that had 3 to 6 h of daily training. This study suggests that, although ET and HR seemed to share a similar physiological basis, the factors that most influenced each parameter were different. Finally, ET seems to be a suitable tool for assessing stress during show jumping competitions in 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".