97 Formulating to Fermentable Protein Can Affect the Health and Performance of Nursery Pigs
Bibliographic record
Abstract
Abstract Two experiments were conducted to evaluate the effects of fermentable protein (FP) on pig health and performance. FP is defined as the difference in ATTD CP and AID CP on a total CP basis. In experiment 1, 1,449 pigs (~19 d of age; initial BW = 5.9 ± 0.2 kg, 16 reps/trt, 22–23 pigs/pen) were blocked by pen location and randomly assigned to one of 4 treatments with FP levels of 1.36, 1.26, 1.16, and 1.06 in phase 1 (d 0–11) and phase 2 (d 11–20.5). FP was decreased primarily through the addition of soy protein concentrate (SPC) and the reduction of soybean meal (SBM). In experiment 2, 144 pigs (~21 d of age; initial BW = 4.7 ± 0.7 kg, 8 reps/trt, 3 pigs/pen) were blocked by weight and randomly allotted to one of 6 treatments with FP levels of 1.30, 1.24, 1.20, 1.15, 1.11, and 1.07 for phase 1 (d 0–7) and 1.22, 1.17, 1.13, 1.08, 1.03, and 0.99 for phase 2 (d 7–21). FP was decreased through the incremental replacement of soybean meal with hydrothermal mechanical processed (HTM) SBM. For both experiments, performance data was analyzed as a general linear model. Mortality and removal (M&R) and stool quality were analyzed as generalized linear mixed models, with a binomial or multinomial distribution, respectively. For experiment 1 (Table 1), the reduction in FP with SPC increased ADFI, decreased gain:feed, and reduced the probability of M&R from trial. For experiment 2 (Table 2), reduction of FP with HTM SBM linearly increased ADG, gain:feed, and probability of visually observing a more normal stool. A quadratic effect of reducing FP was also detected for ADG and ADFI. In conclusion, these two experiments highlight that reducing diet FP can influence health and performance of 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 | 0.001 | 0.001 |
| 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 | 0.001 |
| 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".