158 Effects of Increasing Dietary Standardized Ileal Digestible Lysine Levels on Growth Performance of Grow-Finish Pigs That Were Sired by PIC 800 Boars
Bibliographic record
Abstract
Abstract A total of 1,092 pigs (PIC 800 × Camborough) were used to investigate the effects of increasing dietary standardized ileal digestible (SID) Lys on growth performance in 4 experiments covering body weight ranges of 25-45 kg (Exp. 1), 45-73 kg (Exp. 2), 74-91 kg (Exp. 3), and 98-120 kg (Exp. 4). Pigs were placed in 1 room and divided into 2 groups with 42 pens/group and 13 pigs/pen (6 or 7 gilts and 7 or 6 barrows, balanced among treatments). Group 1 was used in Exp. 1 and 3; group 2 was used in Exp. 2 and 4. At the beginning of each trial, on-trial pens were blocked by body weight and randomly allotted to one of 6 treatments (85.0, 90.0, 95.0, 100.0, 107.5, or 115.0% of PIC SID Lys recommendations) in a randomized complete block design. Data were analyzed using generalized linear and nonlinear mixed models with pen as the experimental unit. In Exp. 2 to 4, the competing nonlinear models did not fit the data or improve the fitness compared with the linear models. Greater SID Lys linearly increased (P < 0.05) average daily gain in Exp. 1 and 3 (Table 1). Increasing SID Lys tended to increase gain to feed ratio (G:F; quadratic, P < 0.10) in Exp. 1, and broken-line linear and broken-line quadratic models estimated the maximum G:F at 99.9 and 98.5% SID Lys, respectively. In Exp. 2 and 3, increasing SID Lys also increased G:F (linear, P < 0.10). Growth performance did not differ among treatments in Exp. 4. Results indicated that the optimum SID Lys concentration for 25 to 45 kg PIC 800 pigs was between 98.5 and 99.9% of PIC SID Lys recommendations. More research is needed to validate the optimum SID Lys concentrations for pigs above 45 kg.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".