299 Validation of genomic evaluation on some economically important traits of Canadian purebred pigs.
Bibliographic record
Abstract
This study was performed to validate the accuracy of Genomic Estimated Breeding Values (GEBV) in comparison to traditional EBV calculation for prediction of future performance of pigs on some economically important traits. The traits studied were total number of piglets born (TPB), backfat thickness (BFT), growth rate (age to 100 kg live weight, AGE) and loin depth (adjusted to 100 kg live weight, LDP). Animals were genotyped with Illumina and Affymetrix high-density SNP panels and the number of remaining SNPs after the quality control and excluding the SNPs on the sex chromosomes were 44507, 45364 and 44967 for Duroc, Landrace, and Yorkshire pig breeds, respectively. All genotypes were imputed to the Illumina 70K SNP panel using the FImpute software. The gebv software was used to compute the GEBVs following the equivalent model of VanRaden. GEBVs of validation animals estimated before performance testing in September 2017 were correlated with the de-regressed national EBVs of March 2018 after performance testing. Except for AGE in Yorkshire and Landrace, the GEBV of September 2017 showed 12–63% higher correlation with adjusted phenotypes of March 2018 than September 2017 EBVs. The validation shows very promising results (Table 1) though there needs to be further investigation into the validation of AGE.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".