25 The effects of closely meeting estimated daily lysine and energy requirements for pregnant sows across two pregnancies on sow body weight change and litter characteristics at birth
Bibliographic record
Abstract
Abstract Gestating sows experience varying nutrient and energy requirements throughout gestation and across parities. The objective of the current study was to determine the effects of precisely meeting estimated (daily) energy and Lys requirements for gestating sows over two pregnancies on sow body weight (BW) change and litter characteristics at birth. One hundred and seven sows (average parity 1.4±0.5) were randomly assigned to a precision (PF ; n=52) or control (CON ; n=55) feeding program between day 2 and 9 of gestation and housed in group-pens equipped with electronic sow feeders capable of blending two diets. The PF sows received unique daily blends of two isocaloric diets (2518 kcal/kg NE; 0.80 and 0.20% SID Lys, respectively) while the CON sows received 2.2 kg of a static blend of the dietsto achieve 0.56% SID Lys throughout gestation. After weaning, sows were re-bred and entered the same feeding program as in the previous pregnancy (PF: n=37; CON: n=37; average parity 2.4±0.5). During the first pregnancy, CON sows had greater BW gain in the first trimester (15.1 vs 10.2±1.2kg; P < 0.05), but BW at the end of gestation did not differ. In the second pregnancy, PF sows had a greater BW gain in the second (21.4 vs 14.1±1.7kg; P < 0.05) and third trimesters (32.6 vs 24.7±3.1 kg; P < 0.05), along with heavier BW and greater loin depths at the end of gestation (249.1 vs 232.9±5.6kg and 73.1 vs 70.4±1.1mm, respectively; P < 0.05). The number of piglets born alive, stillborn, and mummified, and litter birth weights did not differ in either pregnancy. Precision feeding of gestating sows reduced BW gain in the first trimester of the first pregnancy and increased BW gain and maternal protein stores (i.e. loin depth) in the subsequent pregnancy, without affecting litter characteristics at birth.
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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".