Stillbirth and preweaning mortality in litters of sows induced to farrow with supervision compared to litters of naturally farrowing sows with minimal supervision
Bibliographic record
Abstract
Objective: To evaluate the benefits of induced farrowing with supervision on rates of stillbirths and preweaning mortality. Materials and methods: A total of 159 multiparous sows were assigned in approximately equal numbers to two groups. Group One sows (n = 75) were induced to farrow using two intravulvar injections of 5 mg prostaglandin F2α administered 6 hours apart on day 114 of gestation (Day 0). Farrowing was supervised, with assistance given as required. Group Two sows (n = 84) were allowed to farrow naturally, with supervision and neonatal care standard for the production facility. All live piglets were weighed at 3 days and 21 days of lactation. Results: Of the Group One sows, 56 farrowed during working hours on Day 1. There were fewer stillbirths per litter in Group One than in Group Two sows (0.4 ± 0.09 versus 1.0 ± 0.17, respectively). There was no effect of treatment on overall preweaning mortality. Weights were greater for Group One than for Group Two piglets at both 3 days of age (1.9 ± 0.04 kg versus 1.7 ± 0.02 kg, respectively; P < .01) and 21 days of age (5.7 ± 0.06 kg versus 5.5 ± 0.05 kg, respectively; P < .01). Implications: Inducing farrowing and providing supervision on the day of farrowing can reduce stillbirths. However, reducing overall preweaning mortality requires more than 1 day of supervision.
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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".