247 The Effect of Feeding Low Complexity Diets Contaminated with Deoxynivalenol and Supplemented with Nutramixtm or Fish Oil on Nursery Pig Growth Performance
Bibliographic record
Abstract
Abstract Three hundred twenty newly weaned pigs (6.7±0.3 kg BW) were used to determine the effect of low complexity diets contaminated with deoxynivalenol (DON) and supplemented with NutraMixTM or fish oil on nursery pig growth performance. Pigs were randomly divided into 40 pens and assigned to 1 of 5 dietary treatments (n = 8): [1] high-complexity diet containing animal proteins (HC) or one of four low complexity diets with protein supplied only by corn and soybean meal with [2] no DON contamination (LC), or [3] DON contamination of 3 ppm without supplements (DON-), [4] with NutraMixTM supplementation (2 g/kg; DONNM), or [5] with fish oil supplementation (2.5%, as-fed; DONω3). Diets were fed over two phases (7 and 15 days, respectively) and a common phase III diet was fed to all pigs for 20 days. In phase I, ADG, ADFI, and G:F were not different between pigs fed the HC and LC diets, but were lower for pigs fed DONNM and DONω3(P < 0.05). In phase II, pigs fed the DON- and DONω3 diets had lower ADG than LC (375 vs. 410 g/d; P < 0.05) and lower ADFI than HC (452 vs. 519 g/d; P < 0.05), while pigs fed DON- and DONω3 had greater G:F than those fed HC (0.83 vs. 0.78; P < 0.05). The BW at the end of phase II were not different between HC and LC (13.0 kg), but tended to be less for DONω3 (12.6 kg; P = 0.084 and 0.079, respectively). In phase III and over the entire nursery period, there were no treatment effects on ADG, ADFI, G:F, or final BW (26.0±0.7 kg). Feeding low complexity diets contaminated with 3 ppm DON initially reduced growth performance, but pigs were still able to achieve BW not different from HC pigs at the end of the nursery period, regardless of supplementation.
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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) | 0.000 | 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 | 0.001 |
| 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".