168 Use of Genomic Tools to Improve Production Efficiency, Health Resilience and Carbon Footprint of Beef Production
Bibliographic record
Abstract
Abstract The aim is to present validation studies that demonstrate the benefits of genomic retained heterozygosity, genomic enhanced expected progeny differences (gEPDs) for feed efficiency and carcass traits, as well as DNA pooling technologies, to the beef industry. Team members of Livestock Gentec are global leaders in beef genomics research as evidenced by their leadership roles on the Canadian Cattle Genome Project, 1,000 Bull Genomes Project, gEPDs for Commercial Cattle Project and the Functional Annotation of ANimal Genomes initiative. These large-scale projects have created databases of 380 whole bovine sequence genomes, >24,000 cattle genotypes imputed to sequence variants using Run 6 genotypes from 1000 Bull Genomes project, and >20,000 cattle with associated phenotypes for feed efficiency, carcass quality, cow fertility and methane emissions. The use of admixture analysis, genome wide association studies, and genomic prediction have resulted in new genomic tools that aid in mate selection, improve herd heterosis, female fertility, lifetime productivity and health resilience, and improve accuracy (acc. >0.36) of gEPDs for 18 traits in crossbred cattle. Genomic retained heterozygosity has a benefit of $161/female over five parities while decreasing morbidity of calves and improving the carbon intensity of beef production. Multi-trait selection studies using gEPDs for residual feed intake (acc. > 0.35) have demonstrated annual rates of genetic progress of 0.7%. Validation studies have reported that sires with superior gEPDs for increased marbling, decreased grade fat, increased ribeye and increased carcass weight (acc. > 0.45) produced progeny with improved AAA retail cut yield (59.9 vs 56.7%). DNA pooling shows potential for cheaper genotyping while providing information on pooled records related to sire contribution, heterosis and performance as influenced by genetics. The application of these genomic tools has potential to improve calf crop percentage, health resilience, and retail cut yield while decreasing the carbon footprint of beef production.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".