PSVII-39 Impact of reducing dietary fermentable protein level on growth performance in nursery pigs
Bibliographic record
Abstract
Abstract Inclusion of highly digestible proteins can reduce protein fermentation in the hindgut and ameliorate digestive stress after weaning. This study evaluated the impact of reduced fermentable protein (FP) level on growth performance in nursery pigs. A total of 1,760 pigs (~19 d of age; initial BW = 5.4 ± 0.1 kg) were used in a study with 5 dietary treatments, 16 pens/treatment, and 22 pigs/pen. The pigs were housed in two barns which were blocked separately by pen location and randomly allocated to treatments. Treatments were: 1) Corn, soybean meal diets with antibiotics and pharmacological levels of Cu (235 ppm) and Zn (3000 ppm) (PC); 2) Corn, soybean meal diets without antibiotics and nutritional levels of Cu and Zn and standard level of FP (NC); 3) NC + 7.5% of FP; 4) NC - 7.5% of FP; 5) NC - 15% of FP. The content of FP for each ingredient was calculated by subtracting the apparent ileal digestible CP from the apparent total tract digestible CP. Treatment diets were formulated to similar energy and nutrient levels that met or exceeded NRC (2012) requirements. Pen weights were obtained on d 0, 8, and 22.5 post-weaning and growth performance parameters were calculated. Data were analyzed using PROC MIXED in SAS 9.3 (SAS Inst. Inc., Cary, NC). Constructed contrasts tested the effect of antibiotics, Cu, and Zn (PC vs. NC) and level of FP (+7.5, NC, -7.5, -15%; linear and quadratic). From d 0 to 22.5 post-weaning, the NC had reduced BW, ADG, ADFI, and G:F when compared to the PC (P < 0.05). As the level of FP decreased, there were linear increases in BW, ADG, ADFI, and G:F (P < 0.01). In summary, reducing fermentable protein in the diet by using high quality protein sources improved growth performance of nursery piglets.
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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".