166 The Impact of Dietary Crude Protein Level on Growth Performance of Growing-Finishing Pigs
Bibliographic record
Abstract
Abstract Two experiments were conducted to evaluate the impact of dietary crude protein (CP) levels on growth performance of growing-finishing pigs. In experiment 1, a total of 1,035 pigs (47.53 kg ± 1.12 kg) were allotted to a randomized complete block design with 5 dietary treatments (9 pens/treatment; 23 split sex pigs/pen) and fed experimental diets for 5 wk. The concentration of CP in each diet was 13.35, 14.10, 14.85, 15.60, and 16.35%, respectively, and CP was manipulated by soybean meal and crystalline amino acid inclusion. Diets were formulated to be isocaloric and contained equal provisions of standardized ileal digestible Lys, Met+Cys, Thr, and Trp. Diets met or exceeded NRC (2012) estimated nutrient requirements. In experiment 2, the same pigs as used in experiment 1 were randomly reallotted (initial body weight = 107.56 ± 1.77 kg) by pen following a washout period to 5 dietary treatments containing 10.70, 11.45, 12.20, 12.95, or 13.70% CP, respectively. Experimental diets were fed for 3 wk. Results of both experiments were analyzed using the lme4 package of R 4.0.2, and the statistical models included the fixed effect of CP and the random effect of body weight block. Orthogonal contrasts were used to test linear and quadratic effects of increasing dietary CP. Results of experiment 1 indicated final body weight, average daily gain (ADG), and gain:feed (G:F) linearly increased (P < 0.05) as CP in the diet increased (Table 1). Average daily feed intake (ADFI) did not differ among treatments. In experiment 2, dietary CP did not affect body weight, ADG, nor ADFI. As dietary CP increased, however, G:F tended (P < 0.10) to increase. In summary, greater dietary CP improved the performance of growing-finishing pigs by increasing ADG and G:F in the grower phase and tended to increase G:F in the finishing phase.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".