PSIII-2 Considering a large creep pellet as a means to improve suckling and early weaned pig performance
Bibliographic record
Abstract
Abstract This study evaluated the benefit of creep feeding with large pellets (10mm diameter, Masterfeeds, London, ON, Canada) on pre- and post-weaning pig performance. Litters from two farrowing groups were assigned to one of 2 treatments: Control (no creep n= 25 litters) and Creep (large creep pellets n = 24) during the suckling phase. Pellets were provided beginning 7 d after the first sow farrowed within a group until weaning at 21 d. Pellets were placed in shallow plastic feeders three times/day (100-150 g at a time as needed) to ensure access to fresh feed. Creep pellets contained 2500 ppm zinc and ferric oxide-dyed biscuit crumbles were added at 5% inclusion in Phase 1 nursery diet. Therefore, fecal zinc concentration at 2 to 3d prior to weaning and red fecal coloration 2 to 4d after weaning were used to identify “eaters” from “non-eaters”. Pigs were weaned at 21 ± 2 d into pens based on suckling treatment. Data were analyzed as a randomized complete block with the sows as a random effect (PROC MIXED, SAS Inst., Inc., Cary, NC). Post-weaning fecal swabs data were analyzed by frequency test using the PROC FREQ procedure in SAS. There was no difference in pig weight prior to creep pellets. At weaning, Control pigs were heavier due to greater daily gain (P < 0.01; Table 1) than Creep pigs. However, there was no difference in d34 BW due to greater (P < 0.05) daily gain of Creep pigs in the post-weaning period. A greater proportion of Creep pigs were identified as “eaters” on d2 post-weaning (43 vs 33%, □ 2 = 0.004; Figure 1). Lighter weight at weaning in Creep pigs may be due to temporary distraction from suckling; however, exposure to large creep feed pellets pre-weaning improved pig feed intake and growth post-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 | 0.000 | 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".