Count Bayesian models for genetic analysis of in vitro embryo production traits in Guzerá cattle
Bibliographic record
Abstract
Four models for in vitro embryo production traits in Guzerá cattle were compared: Gaussian (untransformed variable - LIN and transformed in logarithmic scale - LOG), Poisson (POI) and zero-inflated Poisson (ZIP). Data consisted of 5716 ovum pick-up and in vitro fertilization records performed in 1205 cows from distinct regions of Brazil. Analyzed count traits were the number of viable oocytes (NOV), number of grade I oocytes (NGI), number of degenerated oocytes (NDG), number of cleaved embryos (NCLV) and number of viable produced embryos (NEMB). Heritability varied from 0.17 (LIN) to 0.25 (POI) for NOV; 0.08 (LOG) to 0.18 (ZIP) for NGI; 0.12 (LIN) to 0.20 (POI) for NDG; 0.13 (LIN) to 0.19 (POI) for NCLV; 0.10 (LIN) to 0.20 (POI) for NEMB depending on the considered model. The estimated repeatability varied from 0.53 (LOG) to 0.63 (POI) for NOV; 0.22 (LOG) to 0.39 (ZIP) for NGI; 0.29 (LIN) to 0.42 (ZIP) for NDG; 0.42 (LIN) to 0.59 (POI) for NCLV; 0.36 (LIN) to 0.51 (POI) for NEMB. The goodness of fit, measured by deviance information criterion and mean squared residuals, suggested superiority of POI and ZIP over Gaussian models. Estimated breeding values (EBV) obtained by different models were highly correlated, varying from 0.92 for NOV (between LIN-POI) and 0.99 for NGI (between POI-ZIP). The number of coincident animals on the 10% top EBV showed lower similarities. We recommend POI and ZIP models as the most adequate for genetic analysis of in vitro embryo production traits in Guzerá cattle.
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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 | 0.013 | 0.030 |
| 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 | 0.004 | 0.002 |
| Research integrity | 0.002 | 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".