The impacts of a fibrolytic enzyme additive on digestibility and performance in the grower and early finisher period, and supplemental <i>Saccharomyces cerevisiae</i> on performance and rumen health in the late finisher period for feedlot cattle
Bibliographic record
Abstract
Two experiments were conducted to determine the effects of a fibrolytic enzyme pretreatment on growth performance, apparent total tract digestibility, and ruminal pH throughout the grower and early finisher period (exp. 1), and to examine the impact of Saccharomyces cerevisiae supplementation on intake, performance, and indicators of gut health in the late finisher period (exp. 2). A total of 54 steers were randomly assigned to a subgroup determining experimental treatment groups. In exp. 1, steers were randomized to control (CON1; no enzyme) or enzyme [ENZ; 0.75 mL·kg−1 dry matter (DM) of feed] dietary treatments. Digestibility was improved (P ≤ 0.05) in ENZ steers for DM, crude protein, net energy for gain, and sugars but did not affect (P ≥ 0.12) dry matter intake (DMI), average daily gain (ADG), or reticulo-ruminal pH. In exp. 2, the treatments were control (CON2; no yeast) or yeast (YST; 3.0 g·animal−1 daily) supplemented diets. Rumen papillae were collected for mRNA expression of gut barrier function (OCLN, CLDN, ZO1, and ZO2) and immune response (TLR2, TLR4, and FCAR) genes and histological measurements. Yeast supplementation decreased (P < 0.001) DMI by 31%, reduced variation in DMI, and improved feed conversion ratios but did not impact rumen health mRNA expression or histology measures (P ≥ 0.07). Overall, enzyme supplementation improved the digestibility of some nutrients in the grower period, and yeast supplementation improved feed efficiency, without impacting growth performance or gut health.
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) | 0.001 | 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) | 0.001 | 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".