PSIV-26 Late-Breaking: Evaluation of a genome-based sorting system for feeder cattle.
Bibliographic record
Abstract
This study evaluated the use of molecular breeding values (MBVs) for carcass traits to sort steers into quality grid and lean meat yield groups. A training set of 2300 animals with genotypes and phenotypes was used to predict MBVs for lean meat yield (LMY) and marbling score (MBS) for 299 Angus, 181 Charolais and 638 Kinsella composite steers using the genomic best linear unbiased prediction method. Steers were placed into MBV-Quality, MBV-Lean, MBV-Marbling and MBV-Other groups on the basis of predicted MBV for LMY and MBS being jointly greater or less than the mean MBV values for both traits. Carcass phenotypes of the steers were then collected and evaluated for consistency with the assigned group using descriptive statistics. Also, the accuracy of genomic predictions was assessed as the additive genetic correlation from a bivariate animal model that fits the observed carcass phenotype and the predicted MBV. The number of steers that met the expected carcass outcome was counted to produce actual percentages for each MBV group. Results showed that on average, MBV-Quality and MBV-Marbling groups had heavier carcasses, greater backfat and more marbling across the three populations while MBV-Lean had leaner carcasses. For most traits, the coefficient of variation suggested less variability and uniform carcass in the MBV-Marbling followed by MBV-Lean and MBV-Quality groups. Greater than 70% of the steers in the MBV-Quality, MBV-Lean and MBV-Marbling groups reached the desired carcass outcome of 70:70 Quality Grid and Y1-lean meat yield in Angus and Charolais but not for Kinsella composite. The accuracy of genomic prediction showed that the MBVs could predict from 60 to 80% of the observed carcass trait values. Thus, genomic profiles can potentially help to streamline the feedlot-finishing process and improve the quality and consistency of beef carcasses.
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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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".