Environment factors affecting racing performances of Thoroughbred horses in Algeria
Bibliographic record
Abstract
The results of the flat races organized in Algeria from 1995 to 2007 by the Algerian Horse Racing Society were used to estimate non-genetic parameters of racing performances of Thoroughbred horses. Performances were assessed through two earnings traits (the logarithm of annual virtual earnings: LAEV and the logarithm of average annual virtual earnings per start: LAEV/S) and a normalized ranking (PERF). The phenotypic correlations between these traits were calculated, in order to deduce what is common and what is specific to each measure. The environment factors that were investigated are age (3 to 8 years and older), sex (male or female), year of race (1995 to 2007) and the interactions between these factors. The General Linear Model (GLM) procedure from SAS software was used to identify and quantify the non-genetic factors affecting racing performances. The results showed significantly high positive correlations (p<0.001) between the three traits, hence considered as accounting for similar aptitudes. The effects of age (with a plateau between 4 and 5 years) and year (with an increasing trend for more recent years) turned out to be significant (p<0.001) for the three traits, the sex effect was only significant for the PERF trait (with better performances for males than females) and an interaction between the age and year of the performance was the only significant interaction (p<0.05) for the LAEV trait. The significant effects of these non-genetic factors indicate the need to adjust the earnings and ranks in the context of a program for genetic improvement of Thoroughbred horses in Algeria.
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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 | 0.001 | 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".