Direct-Fed Microbial Supplementation on the Performance of Dairy Cattle During the Transition Period
Bibliographic record
Abstract
The influence of a direct-fed microbial (DFM) on the prepartum period and the effects on production performance during the postpartum period was investigated using 64 multiparous Holstein cows. Two close-up dry cow diets were fed to two groups of 32 cows each starting 21 d precalving as follows: 1) no DFM and 2) DFM. Post-calving cows were fed a lactation ration with or without DFM supplementation.The direct-fed microbial (DFM) supplement contained 2 × 109 viable yeast cells and 5 × 109 cfu of bacteria (Enterococcus faecium) per cow per day, top dressed in a 90-g supplement [corrected].The DMI during the prepartum period was not affected by DFM supplementation. During the postpartum period, DMI, milk yield, and milk protein content were higher for cows receiving DFM supplementation compared with no DFM. Blood glucose and insulin levels were higher and NEFA levels were lower for cows receiving DFM during the postpartum period. These data suggest that targeted DFM supplementation increased DMI and milk production postpartum. Blood metabolite information would suggest this response was associated with more glucose being made available and less fatty acids being mobilized from lipid stores.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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) | 0.001 | 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".