PSIV-15 Determining the effects of increasing levels of xylanase in nutrient adequate diets on growth performance, carcass characteristics of growing-finishing pigs
Bibliographic record
Abstract
Abstract A total of 1,944 mixed sex growing-finishing pigs (PIC; 337×1050; initial BW of 22.5±0.53 kg) were used in a 131-d growth trial to determine the effects of increasing levels of xylanase in adequate diets on grow-finish pig growth performance and carcass characteristics. The 6 dietary treatments consisted of corn, soybean meal, and dried distillers grains with solubles with added xylanase (Belfeed B 1100 MP; Jefo Nutrition Inc., Saint-Hyacinthe, Quebec) formulated to: 0, 5, 10, 20, 40, and 75 of enzymatic activity for xylanase (IU/kg) with 27 pigs per pen and 12 replicates per treatment. Data were analyzed as a randomized complete block design using lmer function in lme4 package in R with pen considered as the experimental unit, body weight as blocking factor, and treatment as a fixed effect. From d 0 to 70, there was a tendency (quadratic, R=0.068) for average daily gain (ADG) to decrease and then increase with increasing added xylanase, but there was no evidence (R >0.10) of differences for average daily feed intake (ADFI) and feed efficiency (G/F). From d 70 to 131 and overall, there was no evidence of difference (R >0.10) observed for ADG, ADFI, and F/G. There was no evidence for difference (R >0.10) between treatments for number of pigs receiving injectable treatments or mortalities. For carcass traits, increasing xylanase increased then decreased (quadratic, R=0.010) percentage carcass yield. Also, as xylanase increased, percentage lean decreased (linear, R=0.038) and backfat marginally increased (linear, R=0.066). In conclusion, adding increasing levels of xylanase in nutrient adequate diets did not influence growth performance or mortality but did improve carcass yield when intermediate levels were fed.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".