Rumen fermentation and microbial yield of high- or low-protein diets containing ground soybean seeds or homemade rapeseed expellers evaluated with RUSITEC
Bibliographic record
Abstract
Guadagnin, M., Tagliapietra, F., Cattani, M., Schiavon, S., Worgan, H. J., Belanche, A., Newbold, C. J. and Bailoni, L. 2013. Rumen fermentation and microbial yield of high- or low-protein diets containing ground soybean seeds or homemade rapeseed expellers evaluated with RUSITEC. Can. J. Anim. Sci. 93: 363–371. This experiment aimed to compare diets containing two crude protein (CP) concentrations [147 or 109 g kg−1in dry matter (DM)] and two protein sources containing ground soybean seed (GSS) or rapeseed expeller (RSE). Diets were compared in terms of digestibility, volatile fatty acids (VFA) and ammonia concentrations, and N flows, using rumen simulation fermenters (RUSITEC). Home−made RSE (CP=287 g kg−1dry matter and ether extract=199 g kg−1DM) was produced using equipment adopted by small farms. Reduction of dietary CP content did not affect digestibility, except for a reduction of N apparent digestibility (P<0.01), but increased efficiency of N utilization (P=0.001) without affecting microbial N production (P=0.82). Total VFA concentration was not (P=0.56) influenced by CP content. Compared with GSS, RSE exhibited a greater neutral detergent fibre digestibility (P<0.01), it did not influence total volatile fatty acids (VFA; P=0.10) but decreased the proportions of acetate and propionate on total VFA (P<0.001) and increased those of butyrate and branched-chain VFA (P<0.001). Microbial efficiency was comparable for GSS and RSE. Results suggest that reduction of dietary CP concentration in DM did not impair in vitro digestibility and microbial growth. The protein mixture containing homemade RSE showed in vitro fermentative properties and microbial growth comparable with those of GSS.
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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 | 0.001 |
| 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".