Pedigree analysis of Polish warmblood horses participating in riding performance tests
Bibliographic record
Abstract
Borowska, A. and Szwaczkowski, T. 2015. Pedigree analysis of Polish warmblood horses participating in riding performance tests. Can. J. Anim. Sci. 95: 21–29. The aim of this study was to describe the population structure and genetic diversity in Polish warmblood horse population participating in the performance test. The reference population consisted of 596 stallions and 866 mares that participated in stationary performance tests conducted between 2002 and 2011 in Poland. The pedigree data contained 15 452 individuals. Completeness of pedigree information was assessed by two measures: percentage of animals with both parents known and discrete generation equivalent. Individual inbreeding coefficients, coancestry coefficient, individual increase in inbreeding, founder equivalent, founder genome equivalent, effective number of non-founders and genetic diversity loss were estimated to characterize the genetic diversity of the population. The average number of discrete generation equivalents reached 4.50 for observed stallions and 5.04 for mares, which shows good pedigree completeness. Pedigree analysis showed that 34% of the stallions and 44% of mares had inbreeding coefficients above zero. The average coancestry was 0.39% in the whole tested population. Average inbreeding rate of studied populations was very low (0.46%). The effective number of founders was 560. Generally, the inbreeding rate was low; genetic diversity was observed at a constant level for mares and only a small decrease was noticed for stallion. However, further studies are needed in this area.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 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".