PSXIV-38 Feeding natural probiotic products improved growth performance and health of growing beef steers.
Bibliographic record
Abstract
The objective was to evaluate the effects of feeding two commercial feed additives: Bio-Lac Plus (BL; Bio-Ag Consultants Ltd, Wellesley) and Boviglo (BG; Natures Wave, Milverton) on growth performance of growing beef steers. Both products are naturally sourced feed supplement that contains a lactobacillus fermentation product, plant based enzymes and prebiotics. Seventy-five crossbred steers (initial BW 279 kg) were blocked by BW and randomly assigned to one of five treatments: control (no implant, no additives); implant (IM); implant and antibiotics (IMAT; 330 mg/d monensin + 110 mg/d chlortetracycline); IM and 30 g BL/d (IMBL); and 5 ml BG/d. Diet consisted of 60% corn silage and 40% barley concentrate (DM basis). Steers were housed in individual pens and the experiment was 112 d long. Data were analyzed using MIXED procedures of SAS with treatment as fixed effects and steers as random effects. No treatment effect on DMI (8.1 kg/d) was observed. However, growth performance (final BW, kg; ADG, kg/d; G:F) were highest (P<0.05) with IM (420, 1.28, 0.158), IMAT (414, 1.23, 0.155) and IMBL (417, 1.25, 0.150), intermediate with BG (400, 1.11, 0.136) and lowest with control (388, 0.99, 0.125). Treatment x days on-feed was noticed (P<0.05) with ADG, which was greater (P<0.01) with IMBL (0.99) than control (0.54) or other treatments (0.78) during first 14 days. In addition, the needs for drug treatments during trial were reduced (P<0.01) by treatments, which were 53%, 13%, 20%, 0% and 13%, respectively, for control, IM, IMAT, IMBL and BG. These results indicate a distinct performance in ADG of IMBL versus IMAT during d 14 and 56. Supplementation of BL performed better or at least equal to antibiotics currently used in beef cattle rations and could be an alternative for beef cattle production. Feeding BG improved ADG and feed efficiency versus control animals. Key Words:
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".