PSIV-11 Effects of Phytogenic Feed Additives on Growth Performance Traits of Finishing Swine
Bibliographic record
Abstract
Abstract This study was conducted at a commercial research facility and utilized 1,092 finishing pigs (PIC® 337 × Camborough®) in a RCBD with 3 dietary treatments to determine the effects of commercially available phytogenic feed additives on growth performance traits in finishing swine for the last 35 d prior to marketing. The dietary treatments were as follows: 1) Control; 2) Aromex Pro® (AP; Delacon) fed at 0.01%; and 3) Ambitine® (AM; PMI) fed at 0.1%. Pens with 13 pigs/pen and 0.66 m2/pig were randomly allotted to treatments on the basis of live weight and gender. Pigs had ad libitum access to corn-soy diets that met or exceeded the pig’s requirements (NRC, 2012). Aromex Pro® and Ambitine® were added to the diets at the expense of corn. Dietary treatments started on day 0 (BW = 97.7 kg; SEM = 1.04) and were fed throughout the 35 d trial period. On d 21, the two heaviest pigs per pen were marketed, while the remaining pigs in each pen were marketed on d 35. Pigs and feeders were weighed on d 0, 21, and 35. Data were analyzed by using PROC MIXED of SAS® version 9.4, and pen was the experimental unit. The model included the fixed effect of treatment and random effect of replicate. Pre-planned orthogonal contrasts were used to compare AP vs. Control and AM vs. Control. Feeding AP or AM for the last 35 d in finishing both improved (P < 0.05) ADG by 4%, ADFI by 3% and final BW by 1.6 kg over Control, but did not change feed to gain or mortality. These data demonstrate that phytogenic feed additives can improve growth performance traits in finishing swine.
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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) | 0.002 | 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".