Evaluation of effect of probiotics mixture supplementation on growth performance, nutrient digestibility, faecal bacterial enumeration, and noxious gas emission in weaning pigs
Bibliographic record
Abstract
A total of 180 twenty-eight-day-old weaning pigs [Duroc × (Yorkshire × Landrace)] with an average body weight of 7.62 ± 1.25 kg were used in a 42-day trial to evaluate the effect of probiotics mixture supplementation on weaning pigs. Pigs were randomly allotted to one of four dietary treatments: (1) CON, basal diet, (2) T1, CON + 0.1% probiotics mixture, (3) T2, CON + 0.2% probiotics mixture, and (4) T4, CON + 0.3% probiotics mixture. Increasing dietary inclusion of probiotics mixture levels linearly increased average daily gain (ADG) and average daily feed intake (ADFI) during day 0–7 as well as ADG and gain to feed ratio (G:F) during day 8–21 (p < .05). There was a quadratic effect in improving ADFI during day 0–7, day 22–42, and ADG during day 8–21 (p < .05). In addition, increasing inclusion of probiotics mixture levels in the diets linearly increased (p < .05) dry matter (DM), nitrogen (N), energy digestibility, and faecal Lactobacillus counts and decreased Escherichia coli counts and ammonia (NH3) emission. However, no significant differences were observed in ADG, ADFI, G:F during day 22–42, and day 0–42 except for a quadratic increase of ADFI for day 22-42 (p < .05). Feeding the pigs with the diets containing different probiotics mixture levels did not affect the faecal hydrogen sulfide emission (p>.05). In conclusion, increasing inclusion of probiotics mixture up to 0.3% linearly improved growth performance during day 0–7 and day 8–21. Pigs fed the diets with probiotics mixture suplementation improved the nutrient digestibility, faecal bacterial enumeration, and decreased NH3 emission.HighlightsProbiotics mixture supplementation increased growth performance and nutrient digestibility in weaning pigs.Increased fecal Lactobacillus and reduced E. coli counts.Reduced fecal ammonia emission that can contribute in reducing environmental pollution.
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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 | 0.001 | 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) | 0.001 | 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".