Off track training ameliorates emotional excitability in Purebred Arabian racehorses
Bibliographic record
Abstract
The aim of this study was to compare emotional excitability in purebred Arabian racehorses trained either with a standard method or with additional off-racetrack training. The study was carried out on 46 horses that were trained for racing in a home stud. The control group (CN, n = 23) was trained only on the training racetrack, whereas for the experimental group (EX, n = 23), the training schedule was alternated between work on the training track and off-racetrack training in a forest. The emotional excitability in horses was determined according to the heart rate (HR) and heart rate variability (HRV). The measurements (six times every 30 d) were taken at rest, during grooming and saddling, and during mounting and walking with a rider. The behavior of horses was also assessed. Higher activity of the parasympathetic nervous systems was found in EX horses during procedures preceding the training. This effect disappeared and the results paralleled those of CN horses once the training session with a rider began. The tested modification of the race training had a positive impact on the horse behavior of the horse during grooming, saddling, mounting, and walking with a rider. However, the modification influenced the autonomic system activity of horses only at rest and during the procedures preceding training sessions, whereas the effect was not seen during mounting and walking.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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".