183 A blend of plant extracts (Actifor Pro) potentially reduced methane production in a dual-flow continuous culture system fed bermudagrass hay with or without corn gluten feed
Bibliographic record
Abstract
Abstract Twelve dual-flow continuous culture fermenters (1.95 L) were used to evaluate the effect of a phytogenic feed additive (Actifor Pro, Delacon, Engerwitzdorf, Austria; ACT) on ruminal fermentation and methane production when fed high fiber diets, comprised of bermudagrass hay (BGH) with or without corn gluten feed (CGF). Fermenters were utilized in a generalized randomized block design with a 2 × 2 factorial arrangement of treatments (n = 6): 1) diet (with or without CGF at 22% of diet DM) and 2) additive (with or without ACT at 1.0 g/L ACT). Two 10-d periods were conducted. Overall, comparing to no CGF, supplementation with CGF resulted in lower dry matter, organic matter, crude protein digestibility, microbial efficiency of nitrogen utilization, and methane production (mL of CH4/mol of total VFA), but increased neutral detergent fiber digestibility (all P ≤ 0.05). Diet × additive interactions were observed for molar proportion of acetate and propionate, and acetate-to-propionate ratio (A:P, all interactions P < 0.05), where inclusion of ACT increased acetate molar proportion and A:P (both P < 0.05), and decreased propionate molar proportion in diets with CGF (P = 0.05). A diet × additive interaction was also observed for methane production (ml of CH4/mol of total VFA; P = 0.08), where ACT decreased CH4 production per mol of VFA by 42% (P = 0.04), when only BGH was fed to the fermenters. In conclusion, CGF supplementation in BGH diets reduced methane production and improved fiber digestibility. The decrease in methane production per mol of VFA observed with ACT in the BGH without CGF diet warrants further investigation.
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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 | 0.001 | 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".