Analysis of the Microbiota of Milk from Holstein–Friesian Dairy Cows Fed a Microbial Supplement
Bibliographic record
Abstract
Previous studies of direct-fed microbial (DFM) supplements showed variable effects on the microbiota and physiology of dairy cows. The main aims of this study were to investigate the milk microbiota of cows supplemented with a lactobacilli-based DFM compared to untreated cows; describe the changes; and quantify the association between the taxa and cow productivity. The study followed seventy-five Holstein–Friesian dairy cows supplemented with a DFM over 16 months compared to seventy-five non-supplemented cows. Twenty-five cows from each group were sampled for microbiota analysis. The top taxa significantly associated with the variables were as follows: Age (Mammaliicoccus_319276, Turicibacter), milk production (Turicibacter, Bifidobacterium_388775), DIM (Stenotrophomonas_A_615274, Pedobacter_887417), milk fat percentage (Pseudomonas_E_647464, Lactobacillus), calendar month (Jeotgalicoccus_A_310962, Planococcus), milk protein percentage (Tistrella, Pseudomonas_E_650325), experimental group (Enterococcus_B, Aeromonas), SCC (Paenochrobactrum, Pseudochrobactrum), and trimester of pregnancy (Dyadobacter_906144, VFJN01 (Acidimicrobiales)). These were identified using multivariable analysis. Twenty-six genera were associated with the differences between experimental groups, including Pseudomonas, Lactococcus and Staphylococcus. Microbial taxa that changed in relative abundance over time included Atopostipes, Brevibacterium and Succinivibrio. Many of these genera were also part of the core microbiota. Supplementation with the DFM significantly altered the milk microbiota composition in the dairy cows, highlighting the impact of long-term DFM supplementation on microbial communities.
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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 | 0.000 |
| 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 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".