Genetic Evaluation Weight, Carcass and Stayability in Nellore Females
Bibliographic record
Abstract
ABSTRACT Traits related to growth, carcass quality and stayability are key components in enhancing the profitability and sustainability of Nelore cattle production systems. This study aimed to estimate heritabilities and genetic and environmental correlations for these traits using a Bayesian approach. Data from 94,703 females were analysed for weights at 210, 365 and 450 days of age (W210, W365 and W450), loin eye area (LEA), subcutaneous fat thickness in the loin (LFT) and rump (REFT) and stayability at 48, 54 and 72 months (STAY48, STAY54 and STAY72). Heritability estimates (± standard error) were 0.14 ± 0.03 for LEA, 0.20 ± 0.03 for LFT, 0.43 for REFT, 0.12 ± 0.02 for STAY54, and 0.18 ± 0.02 for STAY72. Moderate heritabilities for W210, W365, W450, LFT and REFT indicate a substantial additive genetic component, whereas lower estimates for LEA and stayability suggest a predominant influence of environmental factors. Genetic trends were generally positive but moderate: 0.14 kg/generation (W210), 1.40 kg/generation (W365), 1.77 kg/generation (W450), 0.016 cm2/generation (LEA) and 0.0081 months/generation (STAY72). In contrast, STAY48 showed a slightly negative trend (−0.0073 months/generation). Direct selection for W450 yielded a genetic gain of 9.837 kg, whereas indirect selection via correlated traits resulted in gains ranging from 0.125 to 9.272 kg. These findings highlight the relevance of environmental effects on traits with low heritability, such as LEA and stayability, and reinforce the effectiveness of selection for weight‐related traits due to their moderate heritability and favourable genetic trends.
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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 | 0.000 |
| 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) | 0.002 | 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".