Production Traits of Holstein Cattle: Estimation of Nonadditive Genetic Variance Components and Inbreeding Depression
Bibliographic record
Abstract
Additive, dominance, and additive by additive components of genetic variance and inbreeding depression were esti-mated for production traits from a group of daughters of young sires from the Canadian Holstein population. First lac-tations of 92,838 cows were analyzed. Three sire and dam models (additive, additive plus dominance, additive plus dominance plus additive by additive genetic effects), all including regression of the trait on inbreeding coefficient of the cow, were used to estimate the effect of inbreeding on production traits. For all production traits, heritability in the narrow sense was overestimated with the simplest model, in which only the addi-tive effect was fitted. Estimates of domi-nance variance were low for all traits,.9 to 3%. Additive by additive components were low for milk, 2.8%, and fat yield, 2.8%. but higher for protein yield, 6.8%, and for fat, 9%. and protein percentages, 8.9%. Estimates of inbreeding depression for the five traits were similar across all models (-25,-.9, and-.8 kg;.05 % and.05 % per 1 % increase in inbreeding for milk, fat, and protein production and fat and protein percentages, respectively). More accurate estimates of additive ef-fects might be obtained with the inclu-sion of nonadditive effects for genetic evaluation. If the estimation of inbreed-ing depression is the only objective, sim-ple models and small random samples of the population may be adequate.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".