Analýza úspěšnosti anglického plnokrevníka v rovinových dostizích ve světě
Bibliographic record
Abstract
The aim of this work was to analyse the population of the English Thoroughbred within its most powerful class on flat races worldwide. The selected population was based on the rankings of the best rated horses with a minimum rating of 115, published by the International Federation of Horseracing Authorities from the year 2004 to 2020. Using multifactor analysis, it was demonstrably proven that based on the assessed country training factor, the group of horses trained in Ireland's had the highest rating of 118.85. According to our results, the suffixes of European authorities had the largest percentage and at the same time had the highest ratings, led by the Irish suffix 118.01. Based on the assessed factor of the stallion suffix, we proved that the best rated horses were with a rating of 118.66, which belonged to the offspring of stallions with the Canadian suf-fix. We also found that statistically highly conclusive, including sex factor, was the most successful group of young stallions aged 3-4 years with an average rating of 118.05. The analysis also evaluated the age factor as highly statistically significant. The group of three-year-old horses with a rating of 117.75 had a higher performance than other age groups. In the analysis of “top stallions”, according to the sex, was the most efficient group of stallions with a rating of 118.6. Based on the assessed distance factor, the highest rating of 118.5 was statistically highly convincingly evaluated similarly at medium distances. Street Cry (IRE) was evaluated as the best stallion, whose offspring had an average rating of 120.46. Galileo (IRE) had the most descendants in the database.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.003 | 0.004 |
| 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; both teacher heads agree on what is shown here.
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".