한우 급여 단백질 사료원의 반추위 내 미생물 발효에 따른 우회 단백질과 반추위 미생물 아미노산 조성에 미치는 영향
Bibliographic record
Abstract
The efficiency of feeding protein for ruminants is determined by the composition of bypass proteins, microbial proteins, feed and microbial amino acid fermentations in the rumen. Experiment 1 was conducted with 15 protein sources [Corn grain (South America), Corn grain (Ukraine), Corn gluten feed (Korea), Corn gluten feed (China 1), Corn gluten feed (China 2), Soybean meal (Korea), Soybean meal (USA), Soybean meal (Brazil), DDGS (USA), Palm kernel expeller (Indonesia), Rapeseed meal (Canada), Lupin (USA), Rice bran (Korea), Distillers grain (Korea), Tapioca (Indonesia)] and measured the bypass protein and bypass amino acids. The bypass ratio was the highest at 59.98% in Tapioca (Indonesia). Tapioca (Indonesia) had the highest bypass total amino acid content of 73.73%. Glu and Ala of tapioca (Indonesia) were the highest at 72.17% and 75.23%. In experiment 2, 15 kinds of typical protein sources for Hanwoo steers, the same as those in Experiment 1, were analyzed for the amonio acid composition of rumen microbes following in vitro rumen fermentation. As a result of the experiment, the pH of rapeseed meal (Canada) was 6.99 (p<0.05), the amount of microbial protein synthesis was 326.53mg/100 mL (p<0.05). The concentration of NH3-N is highest at 48.20mg/100 mL (p<0.05) in soybean meal (China 1). The total VFA concentration of corn grain (Ukraine) was highest at 97.92mM (p<0.05). The total amino acid content of the microbial protein was highest at 52.07% of soybean meal (USA). The amino acid composition was the highest in Glu in all feed sources. The results of the present study provide some fundamental data for typical protein sources commonly used for Hanwoo steers in terms of bypass protein and its amino acid composition and also changes in microbial amino acid when those protein sources were fermented in vitro. This information may also provide insight into the supply of metabolizable protein for Hanwoo that are not available in the current feeding standard in Korea.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".