Effect of Adding Two Types of Sodium Acetate Compounds on Corn Silage Quality and Aerobic Stability
Bibliographic record
Abstract
【Objective】The experiment was aimed to study the effect of two silage fermentative inhibitors-sodium diacetate (SDA) and sodium dehydroacetate (SD) that normally are used as food preservatives on quality and aerobic stability of corn silage 【Method】In the experiment,whole newly-mowed corn plants as raw materials were ensiled after treatment with 0.4% SDA and 0.1% SD,respectively,and then were taken to compare with negative control (no additives used) and positive control (with acidizer added,3 ml·kg-1 LuproMix NC). 【Result】The results of the experiment showed that SD-treated silage had lower values in the quantities of molds and yeasts,dry matter loss (DML),and VBN/TN than both the negative control and the positive control significantly (P0.05). SDA-treated silage had the highest lactic acid and acetic acid concentrations,but its DML and VBN/TN were lower than the negative control,but not significant (P0.05). For aerobic stability,SD-treated silage were the greatest (213 h),and SDA-teated silage (147 h) were poorer than the positive control (LuproMix-treated,187 h),but better than the negative control (118 h). 【Conclusion】Thus,addition of SD or SDA did not affect the quality of corn silage but significantly improved aerobic stability. In terms of nutrient preservation and aerobic stability,SD took advantage over SDA or LuproMix NC. Moreover,SD with lower application rate and less cost could be used as a promising fermentative inhibitor of the silage.
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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) | 0.000 | 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 | 0.000 |
| 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".