Impact of yeast hydrolysate (<i>Saccharomyces cerevisiae</i>) supplementation on the growth performance, nutrient digestibility, fecal microflora, noxious gas emission, blood profile, and meat quality of finishing pigs
Bibliographic record
Abstract
A total of 90 finishing pigs (52 ± 1.46 kg) were randomly assigned to one of three dietary treatments as: TRT1 [CON (basal diet)], TRT2 [CON + 0.05% yeast hydrolysate (YH)], and TRT3 (CON + 0.1% YH) for an 8 wk trial. Each treatment had six replications and five (three gilts and two barrow) pigs per pen. In weeks 4 and 8, YH supplement linearly increased (P < 0.05) body weight. Moreover, average daily gain was also linearly increased (P < 0.05) with YH supplementation in week 4 and the overall trial period. At the end of the trial, apparent total tract digestibility of dry matter, nitrogen, and gross energy was linearly increased in pigs fed YH supplementation. In addition, fecal Lactobacillus counts, insulin-like growth factor 1 (IGF-1) concentration, and the lean meat percentage were linearly increased by YH supplementation. Also, YH supplementation elicited a linear reduction on drip loss during days 3, 5, and 7. In summary, dietary yeast hydrolysate supplement positively enhanced the growth performance, nutrient digestibility, Lactobacillus count and reduced the drip loss traits of finishing pig. Thus, we infer that the application of 0.1% S. cerevisiae YH can enhance the growth efficiency of finishing pigs.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".