Effects of extra feeding in mid-pregnancy for three successive parities on lean sows’ productive performance and longevity
Bibliographic record
Abstract
The aim of this study was to investigate the long-term effects of increasing feeding allowance during mid-pregnancy in sows. A total of 103 PIC pregnant sows (mixed parity) were allocated to two treatments: control (C, n = 49) were fed 2.5-3.0 kg d-1 (12.1 MJ ME kg-1) and extra-fed (E, n = 54) received +2.0 kg d-1 of the same feed from day 45 to 85 of gestation over three consecutive cycles. Body weight, backfat thickness (BF) and loin depth were measured on days 45 and 85 of gestation, farrowing and weaning. Litter and sows performance were recorded during lactation and post-weaning. Overall culling rates were 61 and 67% for C and E groups, respectively. After three cycles, E sows showed a positive BF balance in contrast to C sows (E = +1.46 mm and C= -1.81 mm, P < 0.05). In cycle 3, E sows presented greater piglet birth weights than C sows, being mainly evident in sows that were nulliparous at the onset of the experiment (P < 0.05). Extra-fed sows showed a greater incidence of mastitis-metritis-agalactia syndrome than C sows (P = 0.003). Thus, increasing feeding allowance during mid-pregnancy positively affected BF balance and birth weight in nulliparous, but impaired the sows’ ability to produce milk in the long-term.
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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 | 0.000 |
| 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) | 0.001 | 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".