479 Determining Effects of Residual Feed Intake on Economically Important Production Traits in Crossbreed Beef Cattle.
Bibliographic record
Abstract
Maximizing feed efficiency will result in lower production costs for raising beef cattle, currently a 70% cost expenditure for the industry. Residual feed intake (RFI) is commonly used as a measure of feed efficiency, and represents the difference between actual and expected feed intakes based on an animal’s body weight and growth. Efficient and inefficient animals consume less and more than expected, leading to negative (NEG) and positive (POS) RFI values, respectively. While selecting for RFI can decrease the overall feeding costs, its effect on other production traits remains unknown. The objective of this study aimed to determine the effects of RFI on several production parameters including average daily gain (ADG), tenderness (measured by Warner-Bratzler (WB) shear force of the longissimus dorsi muscle at 7 d post-mortem), marbling score, and scrotal circumference, as a measurement of animal fertility. Data from 2,578 crossbreed beef cattle was analyzed by a 1–way ANOVA using a linear mixed model that included RFI (POS ≥ 0; NEG < 0) as a fixed effect and age as a covariate. Results were expressed as least square means ± SE. The results show RFI had significant effects on marbling score, which was increased in POS compared to NEG animals (0.034 ± 0.015 vs -0.027 ± 0.016; P < 0.01). RFI did not have significant effects on WB shear force (4.88 ± 0.04 vs 4.93 ± 0.05 kg; P > 0.05), scrotal circumference (5.35 ± 0.60 vs 6.39 ± 0.67 cm; P > 0.05), or ADG (1.70 ± 0.02 vs 1.68 ± 0.03 kg/d; P > 0.05). In conclusion, selection for lower RFI (as part of a multi-trait index selection, among other economically important traits) may not affect beef cattle fertility, but may change muscle marbling with a subsequent effect on meat quality.
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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 | 0.000 | 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) | 0.001 | 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".