251 Effects of Cross-Fostering and Number of Litters Used to Create Cross-Fostered Litters on Piglet Pre-Weaning Mortality and Weaning Weight
Bibliographic record
Abstract
Abstract The objective was to determine effects of cross-fostering and number of litters used to create cross-fostered litters on piglet pre-weaning mortality (PWM) and weight. The study was conducted on 2 commercial farrowing facilities using a RCBD (53 blocks; total of 265 litters/3675 piglets). Blocking factors were farrowing day, sow parity, body condition score, functional teat number, and average and CV of piglet birth weight. Treatments involved cross-fostering different percentages of the litter (0% = no cross-fostering; 50% = half cross-fostered, half from the birth sow; 100% = all piglets cross-fostered) and differing number of litters used in cross-fostering. The 5 Treatments were (% fostered/number of litters used): 1) 0%/1 litter; 2) 100%/1 litter; 3) 100%/6+ litters; 4) 50%/4+ litters; 5) 50%/2 litters. Litter sizes at cross-fostering (13 or 14 piglets) were equalized across Treatments within block. Piglets were weighed 24 h after birth (when cross-fostering was carried out) and at weaning (19.2 ± 0.97 d); all PWM was recorded. Data were analyzed using SAS, models accounted for the fixed effect of Treatment and random effects of sow, block, and farm. The 0%/1 litter and 100%/1 litter Treatments were similar (P > 0.05; Table 1) for all measurements, indicating no effect of cross-fostering per se. Litter sizes at 7 and 14 d, and weaning were lower (P ≤ 0.05) for the two 1-litter Treatments compared with the 50%/2 litters Treatment; other Treatments were intermediate. There was no effect (P > 0.05) of Treatment on piglet weight. Pre-weaning mortality was greater (P ≤ 0.05) for the two 1-litter treatments compared with the other 3 Treatments, which were similar (P > 0.05). In conclusion, cross-fostering had no effect on piglet performance, however, PWM was greater for treatments comprised of piglets from a single litter compared with those from several litters.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".