149 A bioactive, mineral-based feed additive improved growth performance in commercial nursery pigs
Bibliographic record
Abstract
Abstract NutriQuest Protect™(NQP) is a bioactive mineral-based feed additive that helps control enteric bacteria in nursery pigs. Previous studies have shown feeding NQP improves growth performance and reduces diarrhea in weanling pigs experimentally challenged with E. coli F18 or K88. Six experiments were conducted following the same procedure to evaluate the effect of feeding NQP on growth performance of commercial nursery pigs. In each experiment, weanling pigs (weaning BW = 5.4 ± 0.05 kg) were housed in pens (27 pigs per pen) randomly assigned to either a control diet (CON) or a diet containing NQP at 2.0 g/kg, resulting in 106 replicated pens per treatment over six experiments. Pigs were fed their respective experimental diets for 22–26 d post-weaning in a two-phase feeding program. Five out of the 6 experiments did not utilize feed medications or pharmacological ZnO supplementation. Data from the six experiments were compiled for meta-analysis using the MIXED procedure of SAS. Pigs fed NQP had greater ADG in each of the six experiments, ranging from a 2.3 to 18.1% improvement, with the meta-analysis showing an average of 7.6% improvement compared with CON (0.22 vs. 0.20 kg/d, P < 0.001). The higher growth rate resulted in 0.3 kg heavier pig BW at the end of the experiment for NQP-fed pigs compared with CON-fed pigs (10.8 vs. 10.5 kg, P = 0.001). Pig ADFI of the NQP treatment was also greater in each of the six experiments with the meta-analysis showing an average of 4.4% improvement compared with CON (0.33 vs. 0.31 kg/d, P < 0.001). Additionally, feed efficiency (G/F) was 3.0% higher in pigs fed NQP compared with CON (0.65 vs. 0.67, P = 0.001). Results from the six experiments indicate that including NQP in the diet of weanling pigs improves growth performance in a commercial production system.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".