PSV-20 Effects of Enterid™ in Lactating Sow Diets on Sow and Litter Performance
Bibliographic record
Abstract
Abstract A total of 196 sows (parity = 2±1.1; farrowed in 5 groups) were used in this study to evaluate the effects of feeding Enterid™ feed additive (a mixture of prebiotics, probiotics, and flavoring compounds, PMI Additives) on sow and litter performance. On day 109 of gestation, sows were moved to farrowing rooms and assigned to one of two dietary treatments, including: control and control + 0.05% Enterid™. Respective diets were fed to the sows from day 109 of gestation to weaning (weaning age: 19±0.13 day). Sow body weight (BW) and backfat thickness were measured at d 109 of gestation, within 24h after-farrow, and at weaning. Sow feed consumption was recorded daily. Individual pig BW was recorded at birth and weaning. A completely randomized design was used to test the effect of diets and ANOVA was performed with mixed models using MIXED procedure in SAS (SAS 9.4, SAS Inc.). Sows fed Enterid™ numerically had greater weaning body weight (BW; 222 kg vs. 217 kg; P = 0.11) and lost less BW (-1.14 vs. -5.38 kg; P = 0.01) and backfat (+0.5 vs. -1.5 mm, P = 0.02) from farrow to weaning compared to the control. In addition, sows fed Enterid™ numerically had greater total lactation feed intake (127 vs 123 kg; P = 0.13) and ADFI (6.64 vs 6.42 kg; P = 0.17). Enterid™ numerically reduced piglet preweaning mortality by 1% (9.15 vs 10.16%). Data of this study suggests that Enterid™ can provide benefits toward improving performance of sows and reducing mortality of preweaning piglets.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| 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".