Evaluation of Post-weaning Efficiency in Nellore-Angus Crossbred Steers through Model Predicted Residual Consumption
Bibliographic record
Abstract
The objective of this work was to evaluate efficiency traits of Nellore-Angus crossbred steers (n = 349) on feed. Steers were fed a grain-based diet beginning at approximately 12 months of age for an average of 140 days. Contemporary groups were born in the fall or spring of 2003 through 2007 in full-sibling embryo transfer families or half-sibling families all sired by the same bulls. Individual intake was measured and weights were recorded to permit calculation of average daily gain. Residual feed intake (RFI) was estimated as the residual of models employing regressions on metabolic mid-test weight and ADG. An additional efficiency metric was also constructed and evaluated: model predicted residual consumption (MPRC). Mixed linear models were used to analyze daily dry matter intake, average daily gain (ADG), metabolic mid-test weight, RFI, and MPRC. Large positive associations of DMI with MPRC and RFI were identified along with low positive associations between metabolic mid-weight with ADG and MPRC. Genome wide association analysis revealed 5 regions associated with DMI, but none for the other traits analyzed. Residual feed intake values varied greatly between the contemporary group value and the overall value for the steers, showing the calculation’s dependency on the reference population. However, MPRC as based upon a standardized population, did not fluctuate. More selection phenotypes and strategies are needed for large-scale improvements in global beef cattle production sustainability. The stability of the MPRC metric could be beneficial for future feed efficiency research across multiple and diverse contemporary groups, and diverse production environments.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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".