Effects of fenugreek (<i>Trigonella foenum-graecum</i> L.) seed extract supplementation in different energy density diets on growth performance, nutrient digestibility, blood characteristics, fecal microbiota, and fecal gas emission in growing pigs
Bibliographic record
Abstract
This study evaluated the efficacy of fenugreek seed extract (FSE) in growing pigs. About 140 growing pigs [(Yorkshire × Landrace) × Duroc] with an average body weight (BW) of 23.70 ± 2.80 kg were used in a 6 wk trial. Treatments were (on as-fed basis) two levels of FSE (FSE0, 0 or FSE0.2, 2 g kg−1 basal diet), and two levels of energy [low-energy diet (LED); 3160 or high-energy diet (HED); 3260 kcal kg−1 metabolizable energy (ME)]. Pigs in HED and FSE0.2 diets had higher final BW, average daily gain, and gain:feed ratio (G:F) compared with in LED, and FSE0 diets, respectively (P < 0.05). Pigs fed the FSE0.2 and HED diets had higher energy (E) digestibility than the FSE0 and LED diets, respectively (P < 0.05). Pigs fed the FSE0.2 diet increased serum immunoglobulin G (IgG), and reduced total cholesterol (TC) concentration than the FSE0 diet (P < 0.05). Hydrogen sulfide (H2S) and ammonia (NH3) gas emission in FSE0.2 diet were lower than that in FSE0 group (P < 0.05). In conclusion, results indicated that dietary supplementation of FSE improved growth performance, digestibility, serum IgG, reduced serum TC, and noxious gas emission 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) | 0.000 | 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 | 0.000 | 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".