3 Effect of Litter Size and Provision of Supplementary Liquid Milk Replacer During Lactation on Piglet Pre-Weaning Performance
Bibliographic record
Abstract
Abstract Recent increases in litter size in commercial sows have been accompanied by higher pre-weaning mortality (PWM) and lower weaning weights. The objective was to determine effects of litter size and feeding liquid milk replacer during lactation (using an automated feeder) on piglet performance. A split-plot design was used with a 2x2 factorial arrangement of treatments: Milk Replacer (MR; main plot; Unsupplemented vs. Supplemented); Litter Size [LS; sub-plot; Low (2 piglets less than functional teat number) vs. High (2 piglets greater than functional teat number)]. Cross-fostering was carried out at 24 h after birth to create treatment litters with similar gender ratio, proportion of cross-fostered piglets, and average and CV of birth weight. Milk replacer was available from 24 h after birth to weaning. Piglets were weighed on d 1 and 20 (weaning) after birth; all PWM was recorded. Growth data were analyzed using PROC MIXED of SAS; PWM data were analyzed using PROC GLIMMIX. Models accounted for fixed effects of MR, LS, the interaction, and random effects of replicate and replicate by MR interaction. There were no MR by LS interactions (P > 0.05) for any measurement. Supplemented compared with Unsupplemented litters had similar (P > 0.05) litter size at weaning and PWM, but greater (P < 0.05) average piglet and total litter weaning weight (Table 1). The High LS treatment had greater (P < 0.05) litter size and total litter weight at d 1 and weaning, but higher (P < 0.05) PWM and lower (P < 0.05) average piglet weaning weight. In conclusion, supplementing piglets with liquid milk replacer increased weaning weight with no effect on PWM, and increasing litter size above sow teat number had negative effects on both PWM and piglet weaning weight.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".