Genetic variation within and between subpopulations of the Australian Merino breed
Bibliographic record
Abstract
Genetic variation within and between Australian Merino subpopulations was estimated from a large breeding nucleus in which up to 8500 progeny from over 300 sires were recorded at eight sites across Australia. Subpopulations were defined as genetic groups using the Westell–Quaas model in which base animals with unknown pedigree were allocated to groups based on their flock of origin if there were sufficient ‘expressions’ for the flock, or to one of four broad sheep-type groups otherwise (Ultra/Superfine, Fine/Fine-medium, Medium/Strong, or unknown). Linear models including genetic groups and additive genetic breeding values as random effects were used to estimate variance components for 12 traits: yearling greasy and clean fleece weight (ygfw and ycfw), yearling mean and coefficient of variation of fibre diameter (yfd and ydcv), yearling staple length and staple strength (ysl and yss), yearling fibre curvature (ycuv), yearling body wrinkle (ybdwr), post-weaning weight (pwt), muscle (pemd) and fat depth (pfat), and post-weaning worm egg count (pwec). For the majority of traits, the genetic group variance ranged from approximately equal to two times larger than the additive genetic (within group) variance. The exceptions were pfat and ydcv where the genetic group to additive variance ratios were 0.58 and 0.22, respectively, and pwec and yss where there was no variation between genetic groups. Genetic group correlations between traits were generally the same sign as corresponding additive genetic correlations, but were stronger in magnitude (either more positive or more negative). These large differences between genetic groups have long been exploited by Merino ram breeders, to the extent that the animals in the present study represent a significantly admixed population of the founding groups. The relativities observed between genetic group and additive genetic variance components in this study can be used to refine the models used to estimate breeding values for the Australian Merino industry.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".