Effects of Harvest Date and Ensiling Additives on the Optimized Ensiling of Silphium Perfoliatum to Prevent Faulty Fermentation
Bibliographic record
Abstract
Silphium perfoliatum, an energy crop with a high fiber content but low concentrations of fermentable carbohydrates, presents challenges for complete fermentation in biogas production. To overcome this, a bioeconomic approach proposes the use of the fibers for paper and board production, which requires high-quality silage with minimal butyric acid, which affects the marketability of the fibers. This study aims to optimize the silaging process of Silphium perfoliatum by investigating the effects of harvest date, bacterial cultures and additives on fermentation results. Laboratory experiments were conducted to evaluate the effect of three harvest dates on fermentation acid composition, with a focus on increasing lactic acid production to inhibit butyric acid formation. Results indicate that an early harvest date (early September) is critical for achieving stable fermentation and minimizing ensiling losses. The addition of sugar-rich additives, such as syrup, was found to be essential, especially for later harvest dates. Despite these interventions, a late harvest (early November) consistently resulted in suboptimal fermentation. The results suggest that optimizing harvest timing and incorporating appropriate additives are key strategies for producing high quality silage and ensuring the suitability of Silphium perfoliatum fibers for industrial applications.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".