Assessment of Lactational Performance in Dairy Cows Receiving a Rumen Protected B Vitamin Blend during Lactation: Part 2: A Regression Analysis of 50 Studies
Bibliographic record
Abstract
A previous series of meta-analyses demonstrated that a protected blend of B vitamins (RPBV: folic acid, B12, pyridoxine, pantothenic acid, and biotin; Jefo, St. Hyacinthe, QC, Canada) improved milk fat and protein yield, with variation in the extent of the response. These results represent additional analyses of the same dataset to determine if the degree of response to RPVB on milk, fat and protein yield might be related to the level of production, lactation number, or days in milk (DIM). Results from 50 on-farm switchback trials conducted in 7 countries between 2005 and 2015 were included in the analysis. All herds participated in monthly milk recording services, and all were Holstein herds. A total of 6483 cows, averaging 163 DIM on the first test date, participated in the studies. Data were analyzed using regression models that accounted for the effects of trial, period, days in milk (DIM) and lactation number on milk and component yield. Milk yield and fat yield increased with B vitamin inclusion, and the extent of change was determined to increase with lactation number (P -protected B vitamin blend = 4.05 + [0.917 × control milk] - [0.0063 × DIM] + [0.246 × Lactation number] (R2 = 0.798) The use of regression models allows changes in milk, fat and protein yields with the rumen-protected B vitamin blend to be more accurately predicted.
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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 | 0.021 | 0.020 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.028 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".