Effect of protein source on microbial protein synthesis and nutrient digestion in beef cattle fed barley grain-based diets
Bibliographic record
Abstract
Four British cross heifers fitted with ruminal and duodenal cannulas were used in an experiment designed as a 4 × 4 Latin square. The basal diet was composed of 90% barley grain concentrate and 10% barley silage (DM basis) with either no protein supplementation (13.6% CP), or an additional 1.2% CP (% of DM) in the form of urea, canola meal (CM) or blood meal (BM). Ruminal ammonia N concentration was highest (P < 0.05) for the urea-supplemented diet (111 ± 18 mg N L-1), but no differences were observed among the control, CM- or BM-supplemented diets (59 to 78 mg N L-1; P > 0.05). Ruminal pH averaged 5.78 and was not affected by protein source (P = 0.97). Canola meal and BM tended (P < 0.10) to increase microbial N flow by 31 g N d-1 or 21% above the control diet. The response of microbial N flow to urea supplementation was intermediate between the control and true protein sources (P > 0.10). Ruminal OM and starch digestion were not affected by the dietary treatments (P > 0.10). In conclusion, barley grain-based finishing diets supplemented with protein sources of varying ruminal degradabilities increased microbial protein supply, but the improvement in microbial protein synthesis had no effect on diet fermentability. Key words: Protein supplements, microbial proteins, barley, beef cattle
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 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".