Average stride length and stride rate of Thoroughbreds and Quarter Horses during racing
Bibliographic record
Abstract
Abstract The main factors influencing speed in athletes are stride length (SL) and stride rate (SR). However, conflict remains whether SL or SR is the key determinant of higher speeds. Quarter Horses (QH) generally reach higher speeds in their races than do Thoroughbreds (TB). However, the influence of SL and SR on this greater speed is unclear. Therefore, the main objective of this study was to compare SL and SR in QH and TB raced in short (sprint) and long (classic) distances. We hypothesized that QH have a higher SR in comparison to TB, and SR decreases as distance increases. Two race distances were analyzed for each breed: QH races of 100.6 and 402.3 m, and TB races of 1,207.0 and 2,011.7 m. Data from 20 horses were obtained, consisting of five horses from each race distance (10 QH and 10 TB). Five individuals watched recordings of each race three times counting the number of strides taken by each winning horse. The SR was calculated using the average number of strides over a given race duration, and SL was determined by calculating the total number of strides over the distance covered. Speed was calculated by dividing the distance by the time of the winning horse. The PROC Mixed Procedure was used to identify statistical differences between breeds, and between distances within the same breed. Results showed that although the SL of the TB was longer in comparison with the QH (P < 0.001), the average SR in QH was higher than in TB (2.88 vs. 2.34 + 0.03 strides/s; P < 0.001). Furthermore, QH classic distance demonstrated a faster speed than TB at either distance (P < 0.001). In conclusion, QH achieve a higher SR in comparison to TB (between 14% and 20% more than TB), confirming the importance of SR in achieving high racing speeds.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".