P5057 Use of genomics to simultaneously improve feed efficiency and meat quality in grow-finish pigs
Bibliographic record
Abstract
For the pig industry, feed is the largest cost of production, with the largest proportion (approx. 74%) consumed in the grow-finish phase. Efforts to improve grow-finish feed conversion will significantly reduce production costs and consequently increase total profitability. Feed efficiency is usually defined as lean growth efficiency evaluated through feed intake, daily gain, loin depth and backfat thickness, but without any focus on pork quality. Better pork quality is another high priority for the pork industry to satisfy consumer demand for an enhanced eating experience. Selection based on pedigree and phenotype have shown that high emphasis on lean growth efficiency improves feed efficiency (lower feed intake and higher lean growth), but also reduces pork quality in terms of less marbling and tenderness, and lower pH and lighter meat color (Suzuki et al. J. Anim. Sci. 2005, 83: 2058–2065; Gilbert et al. J. Anim. Sci. 2006, 85: 3182–3188; Cai et al. J. Anim. Sci. 2008, 86: 287–298; 2008; Lefaucheur et al. J. Anim. Sci. 2011, 89: 996–1010). Improving lean growth efficiency without deterioration of pork quality is now a priority. We have performed large scale genomic studies with industry on both feed efficiency and meat quality for grow-finish pigs. We are utilizing whole genome sequence and different densities of SNP genotypes (60K, 80K and 650K) for genome-wide association studies. Our aims are to investigate the genetic architectures and relationships between these economically important traits and to develop genomic tools to increase genetic gain for both feed efficiency and meat quality. Significant genomic regions and markers have been identified for feed intake, loin depth, backfat thickness, meat color, pH, drip loss and marbling. Preliminary genomic prediction results show high accuracy for most feed efficiency component traits, but somewhat lower for meat quality. We are continuing to optimize the genomic selection methods by making good use of such abundant information to improve prediction power and validate the accuracy of the genomic estimated breeding values.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".