Effects of <i>Saccharomyces cerevisiae</i> fermentation products and subacute ruminal acidosis on feed intake, fermentation, and nutrient digestibilities in lactating dairy cows
Bibliographic record
Abstract
Effects of Saccharomyces cerevisiae fermentation products (SCFP) and subacute ruminal acidosis (SARA) on rumen and hindgut fermentation, feed intake, and total tract nutrient digestibilities were determined in 32 lactating Holstein cows between weeks 4 and 9 of lactation. Treatments included control, 14 g·d−1 Diamond V Original XPC™ (SCFPa; Diamond V, Cedar Rapids, IA, USA), 19 g·d−1 NutriTek® (SCFPb-1X; Diamond V), and 38 g·d−1 NutriTek® (SCFPb-2X; Diamond V). During weeks 5 and 8, SARA challenges were conducted by switching from a 18.6% to a 27.9% dry matter (DM) starch diet. This reduced the rumen and feces pH. The durations of the rumen pH below 5.6 during these challenges averaged 175.0, 233.8, 246.9, and 79.3 min·d−1 for the control, SCFPa, SCFPb-1X, and SCFPb-2X treatments, respectively. Hence, SARA was not induced under the SCFPb-2X treatment. The feces pH during the SARA challenges was lowest during SCFPb-2X, suggesting this treatment shifted fermentation from the rumen to the hindgut. The SARA challenges reduced the total tract digestibility of DM, neutral detergent fiber digestibility (NDFd), and phosphorus, but tended to increase that of starch. The SCFPb-2X treatment increased the NDFd from 52.7% to 61.8% (P < 0.05). The SCFPb-2X treatment attenuated impacts of SARA.
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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 | 0.001 |
| 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 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".