Genetic parameters of racing performance traits of Arabian horses in Algeria
Bibliographic record
Abstract
The data used in the present study were recorded at the Algerian Horse Racing Society for 36492 racing performance of 913 Arabian horses from 1995 to 2007. The aim of the study was to identify the genetic parameters underlying three traits: two earnings traits, namely the logarithm of average annual virtual earnings (LAEV) and the logarithm of average annual virtual earnings per start (LAEV/S), and a ranking trait, the normalized ranking (PERF). To identify the fixed effects to be included in the genetic mixed model, a preliminary analysis was conducted using the General Linear Models (GLM) procedure from SAS software. The effects of age, sex, year and the interaction between year of the race and age and between sex and age were included in the model for the three traits. Afterwards, two random effects, a direct genetic effect of the animal and a permanent environmental effect were included in the mixed model. The variance components and genetic parameters were estimated using the restricted maximum likelihood (REML) procedure with the MTDFREML program. The analyses with this repeatable animal model led to the following estimation of the genetic parameters: for LAEV, heritability was 0.225 (±0.041), while estimate of repeatability was 0.330 (±0.040). For LAEV/S, heritability was 0.164 (±0.027), while estimate of repeatability was 0.215 (±0.022). The heritability for the normalized ranking was higher, 0.369 (±0.054), indicating that this trait might provide faster progress for breeding programs of Arabian horses in Algeria. The repeatability estimate for the normalized ranking was 0.587 (±0.045). The genetic correlation between LAEV and LAEV/S was 0.99, revealing a almost complete genetic dependence between these two traits, 0.69 between PERF and LAEV and 0.79 between PERF and LAEV/S.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".