PSIV-A-10 Effects of Canola Meal Inclusion in Gestation and Lactation Diets with or without Probiotic on sow Performance, Milk Composition and Piglet Performance
Bibliographic record
Abstract
Abstract The experiment was conducted to determine the effects of high dietary canola meal (CM) inclusion in gestation and lactation diets with or without probiotic supplementation on sow and litter performance and milk composition. Seventy-five sows were randomly allotted 1 of 3 diets to give 25 replicates per treatment. Diets consisted of a corn and soybean meal (SBM) control (CTRL) or the CTRL diet with SBM replaced by 300g/kg CM and fed with (CM+) or without (CM-) probiotic ActisafÒ Sc 47 supplementation. Sows BW and backfat thickness were determined on d 80 and 111 of gestation, d 1 post-farrowing, and at weaning on d 21. Milk samples were collected on d 1 and d 21 post-farrowing to determine milk composition. Piglets were weighed on d 0 and at weaning. Data were analyzed using the PROC MIXED procedure of SAS 9.4 for a randomized complete block design. Dietary treatment had no effect on sow performance, milk crude protein and lactose oligosaccharide composition, piglet ADG and weaning weight, and piglet survivability (P > 0.10). Sows fed CM- diet tended to fewer (P = 0.07) weaned piglets compared to sows fed CTRL diet, but no difference was found between sows fed the CM+ and CTRL diets (P > 0.10). At weaning, milk fat content for sows fed with CM+ diet was higher (P < 0.05) and tended to be higher (P = 0.09) compared with those fed the CTRL and the CM- diet, respectively. In conclusion, including 300g/kg CM in gestation and lactation sow diets supported similar sow and piglet performance as those the control diet, without affecting piglet survivability at weaning. Also, probiotic supplementation in CM diet increased sow milk fat content at weaning.
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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) | 0.002 | 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".