PSII-12 The effects of lift crates on sow performance and litter survival
Bibliographic record
Abstract
Abstract In modern sow farms, pre-weaning mortality (PWM) remains a key barrier to achieving peak productivity and optimizing piglet welfare. Therefore, a total of 270 sows (PIC 1050, 3.6 average parity) were used (May to July 2018) to determine the effect of lift crates in farrowing on sow and piglet performance and litter survival in a commercial setting in southwestern Ontario, Canada. Lift crates are driven by postural changes in the sow which activate a hydraulic lift system (Balance Frame; NOOYEN, Deurne, NL) which temporarily raises a movable sow slat 25 cm above the crate floor. Sows were randomly assigned to 2.44 m × 1.83 m farrowing crates that were 1) conventional or 2) lift crates. Within rooms, litters were standardized to 13 or 14 pigs within 24 h after parturition. Daily sow feed intake was measured using electronic lactation feeders (Gestal SOLO; JYGA Technologies, Inc.). Data were analyzed using the MIXED procedure of SAS (Cary, NC). Sow was the experimental unit. No differences (P > 0.22) were observed for number born alive, stillbirths or mummified fetuses. Sow backfat loss (2.2 vs. 2.5 mm), average daily feed intake (5.67 vs. 5.74 kg) and wean-to-estrus interval (5.27 vs. 5.62 d) were similar (P > 0.16) between conventional and lift crate treatments. However, from birth to cross-fostering, lift crates reduced (P < 0.01) piglet deaths by 55% when compared to conventional crates (0.58 vs. 0.32 piglets). Piglet deaths from cross-fostering to weaning did not differ (P = 0.18, 0.68 vs. 0.54 piglets). Number of pigs weaned (12.62 vs. 12.87) and suckled litter weaning weight (75.7 vs. 75.6 kg) did not differ (P > 0.11) across treatments. These results provide evidence that lift crates can reduce the number of piglet deaths and may improve PWM in sow farms.Keywords: crate, piglet, survival
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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 | 0.001 |
| 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 | 0.001 |
| 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".