Comparing Bayesian models for genetic evaluation of oocytes and embryo counts in Dairy Gir cattle
Bibliographic record
Abstract
Abstract Count traits are usually explored in livestock breeding programs and it usually does not fit into Normal distribution, requiring alternatives to adjust the phenotype to estimate accurate genetic parameters and breeding values. Alternatively, distribution such as Poisson can be used to evaluate count traits. This study aimed to compare and discuss the genetic evaluation for oocyte and embryo counts considering Gaussian (untransformed variable – LIN, transformed by logarithm – LOG, transformed by Anscombe – ANS) and Poisson (POI) distributions. The data comprised 11343 total oocytes, viable oocytes, cleaved embryos, viable embryos records of ovum pick-up from 1740 dairy Gir heifers and cows. The genetic parameters and breeding values were estimated by the MCMCglmm package of the R software. The posterior means of heritability varied from 0.40 (LIN) to 0.49 (POI) for TO; 0.39 (LIN) to 0.49 (POI) for VO; 0.30 (LOG) to 0.41 (POI) for CE; 0.19 (LIN) to 0.32 (POI) for VE, respectively. The posterior means of repeatability varied from 0.56 (LIN) to 0.65 (POI) for TO; 0.53 (LOG) to 0.63 (POI) for VO; 0.44 (LOG) to 0.60 (POI) for CE; 0.36 (LOG) to 0.56 (POI) for VE, respectively. Deviance information criterion and mean squared residuals indicated that POI model should be used for genetic evaluation of embryo and oocytes count traits. The Spearman rank correlation between estimated breeding value (EBV) for embryo and oocytes count traits computed by POI, LOG, and ANS models was high (ranging from 0.77 to 0.99), indicating little reordering among the best animals. The POI model is the most adequate for genetic evaluation, resulting in more reliable EBV for Dairy Gir cattle of oocyte and embryo counts traits.
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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 | 0.028 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".