Effect of supplementation of sodium stearoyl-2-lactylate as fat emulsifier in low-density diet on growth performance, backfat thickness, lean muscle percentage, and meat quality in finishing pigs
Bibliographic record
Abstract
This study was conducted to evaluate the effect of supplementation of sodium stearoyl-2-lactylate as fat emulsifier in low-density diet on the growth performance and meat quality of finishing pigs. A total of 84 mixed-sex finishing pigs [(Landrace × Yorkshire) × Duroc] at 112 d of age with an average body weight (BW) of 60 ± 0.75 kg (two gilts and two barrows per pen; seven pens per treatment) were used in a 56 d experiment. Pigs were randomly allotted to one of three treatments based on BW and stratified based on sex. The following three treatments were used (1) control basal diet (T1), (2) low-energy diet (T2), and (3) T2 + 0.1% sodium stearoyl-2-lactylate emulsifier (T3). The supplementation of sodium stearoyl-2-lactylate as fat emulsifier in energy-reduced diet did not have significant effects on growth performance compared with energy-reduced diet without emulsifier, although it slightly increased final BW by 1.45%, average daily gain by 3.3%, gain to feed ratio by 3.77%, and reduced average daily feed intake by 0.64%. The supplementation of emulsifier in energy-reduced diet did not have any adverse effect (P > 0.05) on meat quality attributes, as well as backfat thickness and lean muscle percentage (LMP), compared with energy-reduced diet without emulsifier or basal diet. In conclusion, the supplementation of emulsifier at 0.1% level in low-energy diet did not have significant effects on growth performance, backfat thickness, LMP, and meat quality attributes in finishing 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) | 0.000 | 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".