Effects of Mixing Corn Steep Liquor with Dry Rice Straw in Different Proportions on Fermentation Quality and Nutrient Composition of Yellow Rice Straw Silage Feed
Bibliographic record
Abstract
This experiment was aimed to study the effects of mixing corn steep liquor(CSL) with dry rice straw(DRS) in different proportions on fermentation quality and nutrient composition of yellow rice straw silage feed. The ingredients of the test were CSL with the water content of 54.00% and DRS with the water content of 10.00%. There were 4 groups(groupsⅠ to Ⅳ) according to the mixing proportion of CSL and DRS(1∶1,1∶2,1∶3 and 2∶1,respectively,fresh sample mass proportion),and each group had 10 replicates.Each group added the same number of compound Lactobacillus and adjusted the water content of 60.00%. All materials were detected after fermenting for 60 days at room temperature. The results of the experiment show ed that the number of Lactobacillus,lactic acid and ammonia nitrogen(NH3-N) contents in groupⅡ were significantly higher than those in other groups(P 0.05). GroupⅢ had the highest acetic acid content,which was significant difference from the other groups(P 0.05). The NH3-N / total N in group Ⅳ was the low est,which was significant difference from the other groups(P 0.05). With the increase of CSL supplement,the crude protein content was significantly increased(P 0.05),and the contents of neutral detergent fiber and acid detergent fiber were significantly reduced(P 0.05). Thus,the mixing proportion of 1 ∶ 2 is the ideal mixing proportion of CSL and DRS,which can get better quality new fermentation feed.
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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 | 0.001 |
| 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".