Inclusion of probiotic (<i>Lactobacillus plantarum</i>) in high- and low-nutrient-density diets reveals a positive result on the growth performance, nutrient digestibility, gas emission, and blood profile in growing pigs
Bibliographic record
Abstract
A total of 160 growing pigs (24.69 ± 1.89 kg) were randomly assigned to one of four treatments in a 2 × 2 factorial design with two different levels of nutrient density diet with or without 0.3% probiotic (Lactobacillus plantarum). Each treatment had eight replicates with five pigs (three gilts and two barrows) per pen. At the end of the trial, pigs fed 0.3% probiotic supplement had significantly increased body weight, whereas the average daily gain and gain to feed ratio was significantly increased in both probiotic and high-nutrient density (HD) diet. The nutrient digestibility of dry matter was significantly increased in pigs fed HD diet, whereas nitrogen and gross energy digestibility and blood characters immunoglobulin and lymphocyte counts were significantly increased in both HD and probiotic groups. Inclusion of HD diet with 0.3% probiotic significantly decreased NH3 and H2S gas emission. Moreover, nitrogen and energy showed a significant interaction between probiotic and density diet. In summary, dietary probiotics with HD diet increased the growth performance, nutrient digestibility, blood profile, and reduced gas emission. We suggest that 0.3% probiotic with HD diet could serve as an alternative feed additive to enhance the growth performance of growing pigs.
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 | 0.001 |
| 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".