Effect of glycerol in combination with alfalfa on in vitro gas production and microbial protein synthesis
Bibliographic record
Abstract
Alfonso-Ávila, Á. R., Charbonneau, E., Lafrenière, C. and Berthiaume, R. 2015. Effect of glycerol in combination with alfalfa on in vitro gas production and microbial protein synthesis. Can. J. Anim. Sci. 95: 577-588. This study sought to determine the effect of added glycerol on microbial protein synthesis, ruminal degradation and utilization of alfalfa at different concentrations of nonstructural carbohydrates (NSC), using in vitro gas production. The 2×3 factorial plus one treatment consisted of oven-dried alfalfa with two NSC levels [high: 17.9 (HNSC) or low: 7.4% dry matter (DM) (LNSC)] and three glycerol treatments [control without glycerol, 15% crude glycerol (CG) and 15% pure glycerol (PG)], the additional treatment was LNSC+exogenous sugars (LNSC+ES: LNSC with 5% sucrose+5% starch). Five pre-planned contrasts were evaluated from the seven treatments: (1) HNSC vs. LNSC alfalfa; (2) with glycerol vs. without; (3) interaction of alfalfa and glycerol; (4) CG vs. PG, and; (5) LNSC+ES vs. HNSC. Gas production over 24 h was higher for HNSC than LNSC (202 vs. 179 mL g-1 DM) and with glycerol than without glycerol (202.2 vs. 168 mL g-1 DM). A decrease in the acetate:propionate ratio was observed for HNSC compared with LNSC (2.87 vs. 3.27) and for the addition of glycerol vs. no glycerol (2.78 vs. 3.65). Reduced microbial mass (185.5 vs. 240.5 mg g-1 DM) was observed for CG compared with PG. The LNSC+ES treatment had lower microbial protein synthesis and propionic acid production in relation to HNSC. No significant interaction was observed between the effect of NSC content of alfalfa and glycerol utilization. When effects were studied separately, results indicate that increasing NSC in alfalfa stimulates the synthesis of microbial protein. Also, the addition of glycerol promotes the synthesis of glucose precursors. Finally, the type of glycerol has an impact on results obtained suggesting caution when extrapolating results for PG to CG.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".