351 Neonatal birth weight effects on gilt development growth and first parity reproductive efficiency
Bibliographic record
Abstract
Abstract There has been a great deal of interest in gilt development characteristics that predict gilt growth and reproductive traits and which could be measured and manipulated early in the gilt’s lifetime. The objective of the study was to determine neonatal birth weight effects on gilt development growth performance and parity 1 sow reproductive performance traits. Data were collected from 1,052 litters housed at Circle 4 Farms, Milford, UT. A total of 2,960 crossbred Large White x Landrace maternal line gilts entered the research gilt development unit. Gilts were categorized by their individual neonatal birth weight into 3 groups Group I (≤ 1.1 kg; n = 772), Group II (1.2 to 1.5 kg; n = 1,356), and Group III (≥ 1.6 kg; n = 832). Growth and reproductive trait least square means (±SE) for each birth weight group were analyzed and compared among birth weight groups using PROC GLM. Fixed effects in the model included birth weight, farm, and development diet with the random effect of pen within a room. Neonatal birth weight group was a significant (P < 0.05) source of variation for gilt growth in development, number born alive, and litter birth weight at first parity. Gilts from the largest birth weight group had significantly (P < 0.05) larger BW at 100 (45.1 ± 0.3 kg), and BW 200 days (125.7 ± 0.7 kg), faster average daily gain (0.81 ± 0.005 kg), larger BW at puberty (137.7 ± 0.8 kg), larger BW at farrowing (201.1 ± 1.2 kg), larger BW at post-weaning (195.0 ± 1.0 kg), larger number born alive (11.8 ± 0.1), larger litter birth weights (18.2 ± 0.2 kg). The largest birth weight group tended (P > 0.05) to wean more pigs (9.0 ± 0.2) and have greater litter weaning weights (48.8 ± 1.04 kg) at first parity when compared to gilts from the other two birth weight groups. Improving neonatal birth weight will improve gilt development and productivity through first parity.
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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) | 0.000 | 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".