Genetic parameters for earnings in Quarter Horse
Bibliographic record
Abstract
In this study, we estimated the heritability (h(2)) of earnings in the Quarter Horse in order to evaluate the inclusion of this trait in breeding programs. Records from 14,754 races of 2443 horses from 1978-2009 were provided by Sorocaba Hippodrome, São Paulo, Brazil. All ancestors of the registered horses were included in the pedigree file until the 4th generation. Log-transformed performance measures (LPM) were analyzed for animals aged 2, 3, and 4 years and during their entire career. The h(2) estimates were obtained using a multi-trait model and Gibbs sampling that included the effects of sex, year of race, and animal in all analyses. Five analyses were performed: 1 in which LPM was divided by the number of prizes, 1 in which LPM was divided by the number of race starts, and 3 analyses that included the number of prizes, number of race starts, and both (LPM_cNPS) as covariates. Analysis was performed with and without inclusion of the maternal effect. Models were compared based on the deviance information criterion and LPM_cNPS including maternal effects was found to be the best model. The h(2) estimates and standard deviation obtained using model LPM_cNPS were 0.19 ± 0.08, 0.21 ± 0.08, 0.22 ± 0.09, and 0.21 ± 0.07 for earnings at 2, 3, and 4 years of age and total career, respectively. Our analyses indicate that earnings are subject to selection and can be included in breeding programs to improve the racing performance of Quarter Horses.
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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 | 0.000 |
| 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) | 0.000 | 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; a candidate call from one teacher head, 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".