PSVI-8 Effects of reducing dietary fermentable protein by increasing levels of a hydrothermal mechanical processed soybean meal on growth performance and survivability of nursery pigs
Bibliographic record
Abstract
Abstract Inclusion of highly digestible proteins can reduce protein fermentation in the hindgut and improve gut health. This study evaluated the effects of reducing dietary fermentable protein (FP) by increasing levels of a hydrothermal mechanical processed (HTM) soybean meal (ProvisoyTM, Cargill, USA), optimized for protein digestibility, as a replacement for soybean meal (SBM) on growth performance and health of nursery pigs. In total, 1,840 weaned pigs [6.5 ± 0.1 kg initial body weight (BW)] were allotted to 20 replicate pens per treatment (23 pigs per pen) in 2 barns. Pens were blocked by location within barn and randomly allotted to 4 treatments with 0, 6, 12, and 18% HTM SBM in phase 1 (d 0-10) and 0, 3, 6, and 9% HTM SBM in phase 2 (d 10-23). A low complexity basal diet with 27 and 31% conventional SBM in phase 1 and phase 2, respectively was used. The HTM was added at the expense of conventional SBM to reach the desired HTM treatment levels while maintaining digestible lysine concentrations. All pigs were fed a common phase 3 diet from d 23-39 post-weaning. Pig and feed weights were recorded at the beginning and end of each phase. Data were analyzed using the lme4 package of R 4.1.2, and the statistical model included the fixed effect of treatment, as well as the random effects of barn and location within barn. Mortality and removals as well as pen scouring were analyzed as generalized linear mixed models, with a binomial distribution. Contrasts were used to evaluate the linear and quadratic effect of increased dietary inclusion of HTM SBM. Results indicate that reducing FP by increasing HTM SBM levels did not impact overall average daily gain, average daily feed intake, or final body weight. However, dietary FP tended (P < 0.10) to linearly improve gain:feed from day 0-10 post-weaning as FP decreased but not impact overall gain:feed. In addition, as FP decreased, overall mortality tended (P < 0.10) to decrease linearly and pen scouring was numerically reduced on d 3 and d 6 post-weaning when compared to pigs fed conventional SBM. In conclusion, reducing dietary fermentable protein, by increasing HTM SBM may influence survivability of nursery 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 | 0.000 | 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".