Mathematical Representations of Correlations Among Yield Traits and Somatic Cell Score on Test Day
Bibliographic record
Abstract
Prediction of lactation yields and accuracies of yields for use in genetic evaluation can be improved by including information from test day correlations, especially for milk recording plans that vary in the numbers of milk weights recorded and component samples taken. Daily milk weights for 658 lactations of Canadian cows and monthly test records of milk, fat, and protein yields and somatic cell scores for 500,000 lactations of US cows were used to estimate phenotypic correlations between test days within herd-year. Correlations between daily yields for a designated interval between test days generally were highest for midlactation and were lowest for early and late lactation. Regression (two linear, two quadratic, and interaction effects) on mean DIM and interval between test days predicted correlations with a squared correlation of 0.94 for daily milk yields. Similar relationships were found for US monthly data. Variation in sampling was reduced, computer memory was minimized, and p...
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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 teacher head, 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".