150 A bio-active, mineral-based feed additive improved growth performance and reduced severity of diarrhea in weanling pigs challenged with enterotoxigenic Escherichia coli
Bibliographic record
Abstract
Abstract A mineral-based feed additive, NutriQuest Protect™, was evaluated in five artificial Enterotoxigenic Escherichia coli (ETEC) challenge experiments to determine the effects on pig growth performance, fecal consistency and immune response. The five experiments were conducted following a similar procedure and utilized a total of 232 weanling pigs (19 d of age) assigned to one of three experimental treatments: non-challenged control (NC), challenged control (CC), and challenged pigs fed Protect at 4.0 g/kg (CP) with 36, 36, and 44 pens per treatment, respectively. Pharmacological ZnO or medications were not included in any diets. Pigs were allowed a 7-d adaptation period following weaning, orally inoculated with E. coli K88 or F18 on 0 d post-inoculation (dpi) and 1-dpi. Studies were concluded on 4-dpi. Pig BW and feed disappearance were measured on 0-dpi and 4-dpi. Serum samples were collected on 0 and 4-dpi to measure porcine proinflammatory cytokines. Fecal scores were measured daily over the challenge period. Data from the five experiments were compiled for meta-analysis using the MIXED procedure of SAS. The NC pigs had a greater ADG (0.09 vs. -0.01 kg/d, P = 0.002), ADFI (0.24 vs. 0.21 kg/d, P = 0.09), and final BW (6.8 vs. 6.5 kg, P < 0.05). Diarrhea frequency was significantly higher in CC pigs compared with pigs on CP and NC treatments (28.5 vs. 18.7 vs. 5.3%, P < 0.05). Results from the five experiments suggest that NutriQuest Protect™ improves growth performance and reduces inflammation and diarrhea in weaned pigs artificially challenged with E. coli K88 or F18.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.000 | 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".