PSII-16 Effect of red osier dogwood extract on in vitro digestibility and fermentation characteristics of high-grain diet
Bibliographic record
Abstract
Abstract Red osier dogwood (ROD) is an abundant native shrub plant in Canada and rich in phenolic compounds with antimicrobial properties. The objective of this study was to evaluate the effects of ROD extract supplementation on gas production (GP), DM disappearance (DMD) and fermentation characteristics in batch cultures with varying media pH. The study was a completely randomized design with 4 inclusion levels of ROD extract (0, 1, 3 and 5% of substrate) × 2 media pH (5.8 and 6.5) in a factorial arrangement. Substrate was a high-grain diet (HG) containing 10% barley silage and 90% barley-based concentrate mix (DM basis). Inoculum was obtained from 2 ruminally fistulated beef heifers offered the HG diet. Substrate (0.5 g DM) ground through a 1-mm sieve was incubated for 24 h in 3 replications including each combination of treatments. There was no interaction between media pH and inclusion level of ROD on GP, DMD and fermentation variables. Increased media pH (5.8 vs 6.5) increased (P < 0.01) GP (averaged 198 vs. 389 ml/g substrate), DMD (averaged 51.3 vs. 64.6%), and total volatile fatty acid production (averaged 74 vs. 83 mM). Increasing addition of ROD extract did not affect GP, but linearly (P < 0.05) decreased DMD from 56 to 46% at pH 5.8 and from 69 to 61% at pH 6.5. Increasing ROD extract linearly (P < 0.01) increased the proportion of acetate from 43 to 47% and 47 to 50% at pH 5.8 and 6.5, respectively. Acetate to propionate ratio increased from 1.68 to 1.93 and from 1.90 to 2.10 at pH 5.8 and 6.5, respectively. These results indicated that the decreased DMD along with increased acetate to propionate ratio with addition of ROD extract suggests ROD extract may be beneficial to HG fed cattle for reducing risk of rumen acidosis without negatively impacting fibre digestion.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".