PSX-A-8 Updating Pre-Existing Genetic Evaluations System to Evaluate High-Throughput Data in Purebred and Crossbred Beef Cattle
Bibliographic record
Abstract
Abstract High-throughput technologies are available to aid producers in efficiently and sustainably raising many animals, including automated sensory technologies for phenotype measurement and genomic data. However, uptake of genetic selection by the beef cattle sector has been limited by segmentation of the industry. Therefore, easy to use genetic evaluations systems (GES) that can convert ‘big data’ into real-time and comprehensive results are needed, so that beef cattle producers can utilize the technologies that are becoming available to make accurate breeding decisions. The purpose of this study is to update a pre-existing purebred and crossbred beef cattle GES to become both flexible and efficient in its ability to evaluate high-throughput phenotypic data, and to assess the feasibility of including genotypes in a single-step genetic evaluation procedure (ssGBLUP). Firstly, computational operations required for the calculation of breeding values will be quantified and evaluated. A purebred and crossbred reference population will be assembled using data from Canadian beef breed associations purebred (n = 186,928) and commercial animals (n = 14,406). Multiple breeds will be considered (Angus, Charolais, Hereford, and Simmental), and multiple phenotypes analyzed (birth weight, weaning weight, yearling weight, and calving ease). Key component matrix operations with and without the use of Python Libraries (NumPy) will be evaluated. Computational performance of the different strategies will be compared, including CPU time, and memory allocation. Subsequently, a simulated ssGBLUP will be conducted using a population which mimics that observed in the preliminary analysis. Computational costs associated with the implementation of ssGBLUP will be compared to the existing GES. The results of this study will be directly applied to provide beef producers with the tools to improve the genetics of their own herds. This will facilitate the uptake of technology by the industry, thus increasing the economic value and sustainability 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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.004 |
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".