Estimation of (Co)Variance Functions for Test-Day Yields During First and Second Lactations in the United States
Bibliographic record
Abstract
Co)variance components for milk, fat, and protein yields during first and second lactations were estimated from test-day data from 23,029 Holstein cows from 37 herds in Pennsylvania and Wisconsin using a multitrait test-day model.Canonical transformation was used with an expectation-maximization algorithm.To allow description of (co)variances within and across yield traits and parities, four lactation stages of 75 d were defined for each parity, and the test day nearest the center of each interval was used.Prior to analysis, data were adjusted for lactation curves within lactation stages using all records from all available cows.Data from cows with missing values were excluded to allow a canonical transformation to be used for estimation of (co)variance matrices.Data from 9110 cows were available for canonical analysis of lactations with test days in all lactation stages.(Co)variance functions were used to describe (co) variance structure within and across yield trait and parity.(Co)variance components of biological functions (305-d yield, persistency defined as difference between yields on d 280 and 60, and maturity rate defined as difference between second-and first-lactation yields) were developed from (co)variance functions.Heritabilities ranged from 0.09 to 0.22 for test-day yields, from 0.21 to 0.23 for 305-d yields, from 0.03 to 0.11 for persistencies, and from 0.05 to 0.07 for maturity rates.Phenotypic correlations between first-and second-lactation persistencies were low, but genetic correlations were high.Genetic correlations with maturity rate ranged from 0.11 to 0.61 for 305-d yields and persistencies.
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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 | 0.002 | 0.003 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".