Effect of dietary fermented corn in different energy dense diets on growth performance, nutrient digestibility, ileal microorganisms, and fecal noxious gas emission of growing pigs
Bibliographic record
Abstract
This study evaluated the efficacy of fermented corn (FC) in growing pigs. One hundred twenty-eight growing pigs [(Landrace × Yorkshire) × Duroc)] with an initial body weight of 29.59 ± 1.34 kg were used in a 6 wk experiment. Pigs were assigned into one of four dietary treatments in a 2 × 2 factorial arrangement with two levels of nutrient density (high energy: 3.37 Mcal kg−1, or low energy: 3.29 Mcal kg−1) and FC (0% or 20% substitute for corn). Pigs fed diets with FC increased (P < 0.05) average daily gain (ADG) overall. Pigs fed with high-density (HD) diets increased (P < 0.05) ADG and gain to feed (G/F) ratio overall. Apparent total tract digestibility (ATTD) of dry matter (DM), gross energy (GE), and nitrogen (N) increased (P < 0.05) in pigs fed with FC diets compared with pigs fed the diets without FC. In addition, pigs fed with HD diets increased (P < 0.05) ATTD of DM and GE compared with low-density (LD) diets. Furthermore, pigs fed with HD diets had a higher (P < 0.05) ATTD of DM and GE than those fed with LD diets. Both LD and FC supplementation led to lower (P < 0.05) fecal gas emission content. Pigs fed FC diets increased ileal Lactobacillus concentration and decreased Escherichia coli concentration. An interactive effect between nutrient density diet and FC was observed on the G/F, Lactobacillus on ileal microorganisms, and nutrient digestibility. In conclusion, the results indicated that dietary supplementation of HD diets and FC improved performance in growing 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 | 0.001 |
| 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 | 0.001 |
| 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".