PSII-21 The Effect of Feeding a LucraFit® Feed Nursery Program without Lactose on Piglet Performance
Bibliographic record
Abstract
Abstract The objective of this study was to evaluate the effect of feeding LucraFit® Feed nursery program without lactose on piglet performance. A total of 440 weanling pigs, initial weight of 5.76 ± 0.23 kg., were randomly assigned to either Control or LucraFit® Feed treatments with 11 replicates per treatment and 20 pigs per pen. Pigs were weighed at day 0, 8, 15, 21, 29, and 43 post-weaning with corresponding ADG, ADFI, and G:F calculated for each time period. Data was analyzed using the Mixed procedure of SAS. Pigs were fed a three-phase nursery program with 2.27, 5.44, and 13.19 kg/pig for phases 1-3 respectively. Control diets contained 12.5% lactose in phase 1, 5% lactose in phase 2, and 0% lactose in phase 3. LucraFit® Feed diets contained 0% lactose in all phases. LucraFit® Feed was added at 2.5% and 1.25% in phase 1 and 2 respectively. During d0 to 8, there was no significant difference in growth measurements. During d8 to 15, pigs fed LucraFit® Feed treatment had increased (P=0.039) ADG (0.296 vs 0.266 kg/d) and improved (P=0.035) G:F ratio (0.977 vs 0.923). During d15 to 21, ADG was not significantly different between treatments, however ADFI was tended to increase (P=0.061) with the LucraFit® Feed treatment (0.526 vs 0.497 kg/d) resulting in decreased (P=0.047) G:F ratio (0.783 vs 0.824). Through d21 to 43, LucraFit® Feed tended to improve (P=0.082) G:F ratio (0.676 vs 0.660). Overall (d0 to 43) G:F ratio tended to improve (P=0.082) with the LucraFit® Feed treatment (0.723 vs 0.709). No significant differences were observed in pig removals between treatments. Final (d 43) body weight was not significantly different between treatments. These results suggest that pigs can be started on a LucraFit® Feed nursery program with 0% lactose without having a negative impact on nursery performance.
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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) | 0.001 | 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".