The effect of anaerobic fungal inoculation on the fermentation characteristics of rice straw silages
Bibliographic record
Abstract
AIMS: To identify whether the supplement of anaerobic fungi isolates with cellulolytic activities accelerates the silage fermentation. METHODS AND RESULTS: Three fungal isolates with the highest cellulolytic activities among 45 strains of anaerobic fungal stock in our laboratory were selected and used as silage inoculants. The rice straw (RS) was ensiled for 10, 30, 60, 90 and 120 days with four treatments of anaerobic fungi derived from the control (no fungus), Piromyces M014 (isolated from the rumen of the Korean native goat), Orpinomyces R001 (isolated from the duodenum of Korean native cattle) and Neocallimastix M010 (isolated from the guts of termites), respectively. The silages inoculated with pure strains of fungi showed a higher fungal population (P < 0.05) when compared to the control silage. In situ ruminal DM disappearance of RS silage (RSS) was improved with fungal treatment. SEM observation showed live fungal cells inoculated in RS could survive during the ensiling process. Overall, this study indicated that the inoculation of anaerobic fungi decreased the cell wall content of the RSS and increased in situ dry matter disappearance. CONCLUSIONS: The supplementation of anaerobic fungi isolates to RSS as a silage inoculant improves the RSS quality. SIGNIFICANCE AND IMPACT OF THE STUDY: This is the first study showing the potential application of supplement of anaerobic fungi isolated from the guts may be applied industrially as an alternate feed additive that improves the silage quality.
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 | 0.000 | 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".