Quantitative study of genetic gain for growth, carcass, and morphological traits of Nelore cattle
Bibliographic record
Abstract
The aim of this study was to evaluate the genetic variability, genetic and phenotypic associations, and genetic gains of birth (BW), weaning (WW), and yearling (YW) weights, loin muscle area (LMA), backfat thickness (BF), rump fat thickness (RF), scores of body structure (BS), finishing precocity (FS), and muscling (MS) in Nelore cattle. Genetic parameters were obtained through Bayesian inference using BLUPF90 programs. All studied traits showed genetic variability, with heritability ranging from 0.29 to 0.47. In all studied ages, weights presented positive genetic correlations with LMA (ranging from 0.13 to 0.53), being generally stronger in comparison with the other carcass traits analyzed (BF and RF). Similarly, weights were higher genetic associated with BS (0.47–0.92) than with FS (0.18–0.62) and MS (0.22–0.65), respectively. The BF and RF showed positive and moderate genetic associations with FS and MS (0.31–0.36). Genetic trends were significant (P < 0.05) and favorable for WW, YW, and visual scores. Selection for increasing BW, WW, YW, and LMA will result in modest or no change in BF and RF (correlated response ranging from −0.04 to 0.07 mm per generation). In this population, carcass traits must be included in the selection indexes to obtain genetic gains in carcass quality, if desired.
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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 | 0.001 |
| 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".