205 An Adjusted SID-Thr:Lys Ratio Offsets Growth Performance Depressions in Finishing Pigs Fed Moderately High Distillers Dried Grains with Solubles (DDGS)
Bibliographic record
Abstract
Abstract Two experiments were conducted to determine the impact of dietary SID Thr:Lys ratio (THR) on the performance of grow-finishing pigs. In Experiment 1, a total of 1,152 pigs (initial BW = 21.0 ± 1.4 kg; 12 pens/treatment; 24 pigs/pen) were used in a randomized complete blocked design, with a 2 x 2 factorial arrangement of treatments containing 2 levels of DDGS (0 vs 25%) and 2 levels of THR (Low vs. High, 0.60 vs 0.67). Equal energy and nutrient levels that met or exceeded NRC (2012) requirements were achieved across treatments. A total of 1,056 pigs (initial BW=18.6 ± 1.9 kg; 16 pens/treatment; 22 pig/pen) were used in Experiment 2 with the same experimental design, except for the SID Thr:Lys ratios were formulated to 0.60, 0.60, 0.61, 0.62 and 0.63 in 5 dietary phases for Low THR, and +0.03 for all phases in High THR. The MIXED procedures were employed for statistical analysis. Orthogonal contrasts were applied to evaluate main effects of DDGS, THR, and their interactions. In the results, Experiment 1 showed from 20 to 60 kg of BW 25% DDGS inclusion reduced (P < 0.01) FBW (63.4 vs 61.2 kg), ADG (0.88 vs 0.84 kg/d), and G:F (0.50 vs 0.49). There was an interaction between DDGS and THR on G:F (P < 0.01), where High THR improved G:F in diets containing 25% DDGS. In Experiment 2, reduced ADG (0.88 vs 0.81 kg/d) and G:F (0.53 vs 0.51) were observed for pigs fed 25% DDGS in grower phase (P < 0.01). In the early finisher, increased THR in diets with 25% DDGS improved (P < 0.01) FBW (90.54 vs 94.12 kg), ADG (0.95 vs 1.07 kg/d), and tended to improve G:F (P < 0.10; 0.37 vs 0.41), but not in pigs fed 0% DDGS. In summary, these two studies demonstrated greater SID Thr:Lys ratio may help offset the negative impact of fibrous diets on BW gain and feed efficiency in grow-finishing pigs.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".