Assessment of the Canadian model predicting daily milk yield and milk fat percentage using single-milking dairy herd improvement samples
Bibliographic record
Abstract
The use of adjustment factors with alternate morning (AM) and evening (PM) milk tests to predict daily milk yield and fat percentage from single milking could lead to erroneous daily data. The aims of this study were to evaluate the relationship between predicted daily milk yield or milk fat percentage, calculated using single milking samples and Canadian adjustment factors, and the actual daily milk yield or milk fat percentage, as well as to explore feeding and management variables, that could improve daily predictions. A total of 4277 Holstein cows in 100 dairy herds were enrolled. Separate PM and AM milk samples were collected for each cow using in-line milk meters. Daily milk yield and milk fat percentage predictions were computed from single-milking samples using adjustment factors taking into account milking interval and milking time. Concordance correlation coefficients between actual daily milk yields and daily milk yield predictions from PM (0.970) and AM (0.974) milkings were higher than those between actual daily milk fat percentages and daily milk fat percentage predictions from PM (0.897) and AM (0.917) milkings. There were only slight prediction improvements when days in milk, parity, and some feeding management variables were entered in models aiming to explain residuals.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".