Dietary inclusion of mineral detoxified nano-sulfur dispersion on growth performance, fecal score, fecal microbiota, gas emission, blood profile, nutrient digestibility, and meat quality in finishing pigs
Bibliographic record
Abstract
This study is to evaluate the effects of mineral detoxified nano-sulfur dispersion (DSD) on growth performance, fecal score, fecal microbiota, gas emissions, blood profile, nutrient digestibility, and meat quality in finishing pigs. A total of 160 pigs with an initial body weight (BW) of 54.90 ± 5.10 kg were randomly assigned to two treatments including basal diet and basal diet with 10 ppm DSD. During the 10 wk trial, there were no differences in BW, average daily gain, average daily feed intake, and gain to feed ratio between the control and DSD groups. Also, the fecal score, fecal microbiota, gas emission were not affected by DSD diet. Dietary inclusion of DSD tended to increase water-holding capacity and decrease cooking loss and drip loss. At week 5, serum concentrations of glucose, calcium (Ca), total cholesterol, and high-density level cholesterol were increased, and triglyceride concentration was reduced in pigs fed with DSD than control diets. In summary, the inclusion of dietary DSD in the finishing pig diet has improved serum Ca, glucose concentrations, and lipid profiles as well as improves some meat quality traits.
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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) | 0.000 | 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 | 0.000 |
| 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".