Effect of a feed additive based on a mix of condense and hydrolysable tannins (ByPro®) on lactating dairy cows milk production performance
Bibliographic record
Abstract
Abstract The objective of this research was to evaluate the effect of a commercial mix of condensed and hydrolysable tannins feed additive to lactating dairy cows. Two high-scale dairy farm units, similar management and diets were used. All lactating cows in one unit were fed with the mix of tannins (ByPro ® 0.3%DMI), the other unit was the Control. Cows were categorized per DIM, 0 to 30, 31 to 120, 121 to 365, and more. Milk yield and composition was evaluated in all the lactating cows in each dairy unit at time 0, (initial), and after 4 weeks of supplementation (final). A linear model was adjusted for each variable. Estimated DMI and diets were similar in both dairy units, averaging 25.5 kg DMI/cow, as well as feed ingredients, dietary nutrient contents, and milk yield (34 kg milk/cow per day). Milk yield, from 0 to 365 DIM was increased ( P < 0.01) on supplemented cows (+ 2.3 ± 0.33 L milk/cow.d), with no effect on milk fat and protein contents. High data variability was observed on cows with 0–30 DIM, milk fat and protein content were reduced by ByPro without effect on milk yield. Positive responses of ByPro ® on milk, milk fat and protein contents from 31 to 120 and 121–365 DIM were observed, + 3.6 and + 3.2 kg milk/cow.d; +0.27% fat and + 0.21% protein; and + 0.17% fat, + 0.11% protein; respectively. The mix of tannins increased milk lactation performance on cows with more than 30 DIM.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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".