Reducing the effect of parent averages from animal solutions in mixed model equations
Bibliographic record
Abstract
Summary Selection of animals based on their BLUP evaluations from an animal model results in animals that are closely related which leads to increased rates of inbreeding. The tendency for higher inbreeding rates is greater at low heritability values. Several attempts have been made to reduce the impact of parent average breeding values from animals evaluations in order to reduce inbreeding while not sacrificing genetic response. A method that modifies the rules for forming the inverse of the additive genetic relationship matrix for use in best linear unbiased estimation of breeding values via an animal model was developed. This method and several others were compared analytically and empirically, from the perspective of partitioning the animal solutions into contributions from the data, from progeny, and from the parent average. The ratio of genetic progress to average level of inbreeding showed that the modified relationship matrix method was superior to the other methods. Similar results could be obtained by using artificially high heritability in a usual BLUP analysis.
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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 | 0.009 | 0.034 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".