Effect of inbreeding on the incidence of retained placenta in Friesian horses1
Bibliographic record
Abstract
This study was motivated by the hypothesis that the incidence of retained placenta (RP) in Friesian horses is associated with inbreeding. The objectives were to 1) calculate the inbreeding rate in the total registered Friesian horse population; 2) study the association of the inbreeding coefficient of the foal and the mare with the incidence of RP; and 3) study the heritability of RP in Friesian mares after normal foalings. Data from the total registered Friesian horse population from 1879 to 2000 (52,392 individuals) were collected from the registration files of the Friesian Horse Studbook. In 1999 and 2000, 495 parturitions in 436 mares were studied. From 1979 to 2000, the inbreeding rate of the total population was 1.9% per generation. The regression coefficients for the regression of the incidence of RP on inbreeding coefficients of the foal and the mare were 0.12 +/- 0.052 and -0.016 +/- 0.019, respectively. Mean heritability estimates of RP as a foal trait and as a mare trait were 0.046 +/- 0.088 and 0.105 +/- 0.123, respectively. It was concluded that, in order to avoid a further increase in the incidence of RP in Friesian mares, a decrease in the inbreeding rate by increasing the effective breeding population is required. Furthermore, the findings indicate that the high incidence of RP in Friesian horses is at least partly a result of inbreeding.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".