Analysis of validated and population-specific single nucleotide polymorphism parentage panels in pedigreed and commercial beef cattle populations
Bibliographic record
Abstract
A pedigreed population containing 71 calves and 8 sires was used to compare sire qualification using three genotyping platforms [14 microsatellite, real-time quantitative PCR, and 100, 200, 500, and 1000 single nucleotide polymorphism (SNP) arrays]. Parentage was also qualified in an unknown-pedigree population containing 8480 calves with 460 sires using SNP arrays. The three platforms qualified the true sire in the known-pedigree population with zero mismatches. The 100 and 200 SNP arrays yielded specificities of 0.92 and 0.99 with a 1% mismatch rate in the known-pedigree population, respectively. In the larger population, SNP panels of the 500 and 1000 highest minor allele frequency SNPs were also evaluated. The 1000 SNP panel qualified paternity to a single sire for 82.1% of calves with 1% or 2% mismatches. Not all commercial sires were genotyped, which accounts for missing paternity for some calves. In this larger population, the 100 SNP array qualified multiple sires to 0.42% of calves and single sires to 80.84% of calves without mismatches. The 200 SNP array assigned unique paternity, and 79.8% of calves were qualified to a sire without mismatches. With a 2% mismatch rate, sire qualifications agreed with the 1000 SNP array. This study highlights the interplay among population size, genotyping error rates, and the specificity and sensitivity of parentage platforms.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".