Estimation of genetic parameters for milk yield of cattle by random regression model
Bibliographic record
Abstract
Random regression model was applied into estimation of genetic parameters for milk yield in cattle.Variance components were estimated using Gibbs sampling procedure on Bayesian theory.A total of 768 205 test day(TD) records were extracted for Canadian Jerseys calving between 1988 and 1999.After editing,the calibration sample consisted of 43 661 TD records from 4 686 cows in this study.We nested different order Legendre polynomial within additive genetic effects(5 orders) and permanent environmental effects(7 orders) in the random regression model.Heritabilities of milk yield 0-330 d of test varied between 0.2707 and 0.4291,where heritabilities 0-22 d of test clearly decreased with day of test but ones 22-125 d of test obviously incre-ased with day of teat.In addition,genetic and phenotypic correlations between milk yields at different test day were also obtained using regression model.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".