Effects of a matrix-coated organic acids and medium-chain fatty acids blend on performance, and in vitro fecal noxious gas emissions in growing pigs fed in-feed antibiotic-free diets
Bibliographic record
Abstract
This study evaluated the efficacy of a matrix-coated organic acids and medium-chain fatty acids blend (MCOFA) in growing pigs. Ninety six pigs [(Yorkshire × Landrace) × Duroc] with an average body weight (BW) of 47.71 ± 3.73 kg were used in a 6 wk experiment. Pigs were allotted to diets containing 0 or 2 g kg−1 of MCOFA, and 0 or 2.5 g kg−1 of antibiotic growth promoters (AGP) according to a 2 × 2 factorial arrangement of treatments. Pigs fed diets supplemented with MCOFA had improved growth efficiency compared with those fed a diet without MCOFA (P < 0.05). Pigs receiving the diets supplemented with both AGP and MCOFA had higher apparent total tract digestibility of crude protein, dry matter, fat, and gross energy (P < 0.05). Pigs fed AGP × MCOFA diet had increased serum urea nitrogen (P < 0.05). Pigs fed diets supplemented with AGP had reduced fecal ammonia (NH3) gas emissions compared with those fed without AGP (P < 0.05). Moreover, pigs fed diets supplemented with MCOFA had reduced fecal NH3 and acetic acid gas emissions compared with those fed without MCOFA (P < 0.05). In conclusion, dietary supplementation with MCOFA 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 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".