Racing speeds of Quarter Horses, Thoroughbreds and Arabians
Bibliographic record
Abstract
REASONS FOR PERFORMING STUDY: While Quarter Horses are recognised as the fastest breed of horse, direct comparisons to race times with other breeds can be misleading. Quarter Horse races begin when the starting gates open. Thoroughbred and Arabian races begin a short distance from the gates after horses have started running. This study compared speeds of these breeds as they accelerate from the starting gates and during the middle and end of races. OBJECTIVES: To compare racing speeds of the 3 breeds, and to compare speeds during various segments of the races. METHODS: Video tapes of races were obtained from a local track. The various race segments were viewed and the winning horse timed by 5 individuals. Fastest and slowest times were removed and the 3 remaining times averaged. RESULTS: Quarter Horses averaged faster speeds than Thoroughbreds even when Thoroughbreds were raced at a distance (402 m) similar to Quarter Horse races. Both breeds were substantially faster than Arabians. Quarter Horses racing 336 m or less gained speed in each segment of the race while Arabians and Thoroughbreds racing 1006 m ran fastest during the middle of the race and had decreased their speed in the final segment of the race. CONCLUSIONS: Despite similar race times reported for 402 m, Quarter Horses averaged faster speeds than Thoroughbreds when timed from a standing start. In short races, both breeds accelerate throughout the race. Arabians, despite being known for endurance, had slowed by the end of the race. POTENTIAL RELEVANCE: This study demonstrates that Quarter Horses achieve faster racing speeds than do other breeds. It also reveals a potential flaw in race-riding strategy as a more consistent pace throughout the Arabian and longer Thoroughbred races may be more efficient and result in a faster overall race time.
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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 | 0.002 | 0.010 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".