Genetic parameters and breed differences for feed efficiency, growth, and body composition traits of young beef bulls
Bibliographic record
Abstract
Genetic associations between feed efficiency, growth, and live ultrasound measured body composition traits were studied in purebred beef bulls of six breeds in Ontario bull test stations from 1991 to 2000. Feed traits included average daily feed intake (FI), feed conversion ratio (FCR), and residual feed intake [feed intake adjusted for production alone (RFIp) or production and backfat thickness (RFIb)]. Growth traits were average daily weight gain (ADG), mid-test metabolic weight (MW), hip height (HH), and scrotal circumference (SC). Body composition traits included ultrasound backfat thickness (BF), longissimus muscle area (LMA), and predicted percentage of intramuscular fat (IFAT). Bulls were measured every 28 d for weight and individual feed intake, and at the end of test for ultrasound body composition traits. Number of records per trait ranged from 2284 (FI) to 13 319 (ADG). Fixed effects of test group, breed and end of test age (within breed), and random effects of animal and herd of origin were modeled using REML bivariate analyses for all traits. Heritability estimates were moderate for all traits (0.30 to 0.55), except for IFAT (0.14). The genetic correlation between RFIp and RFIb was high (0.99) within breeds, but breeds ranked differently with respect to RFIp and RFIb. Genetic correlations of RFIb with ADG and backfat thickness were essentially zero, which indicate that selection on residual feed intake could be implemented to reduce feed intake and improve feed conversion without compromising growth or changing levels of subcutaneous fat. Key words: Central test, genetic correlation, heritability, residual feed intake
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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".