The Nutritional Value Enhancement of Oil Palm Empty Fruit Bunches as Animal Feed Using the Fungus Coprinus Comatus, with Different Numbers of Inoculums and Incubation Times
Bibliographic record
Abstract
This study aimed to increase the nutritional value of oil palm empty bunches (EFB) as ruminant animal feed by using biological treatment. To achieve this, five fungi species were used, including Trametes Versicolor, Lentinula edodes, Coprinus comatus, Pleurotus sajor-caju, and Trichoderma sp, which were inoculated for 20 days. Furthermore, the study consisted of 2 stages, in the first, the five species were tested for their degradability to lignin. In the second, the results were analyzed for their degradation ability by treating several numbers of inoculums (0.5 ml, 0.75 ml, and 1.0 ml) at different incubation times (20, 30, and 40 days). The results showed that the fungi treatment gave different lignin levels of oil palm empty fruit bunches compared to others. Furthermore, treatment with Coprinus comatus fungi produced the lowest lignin and the highest cellulose levels than others. This species works well compared to other fungi in the delignification of oil palm empty fruit bunches. With the use of Coprinus comatus, the lowest lignin and highest cellulose levels were obtained in a 0.5 ml inoculum treatment and at 30 days incubation time, however, there was no interaction. Conclusively, this study indicated that the application of Coprinus comatus to oil palm empty fruit bunches reduces lignin levels and increases cellulose by 22.04% and 20%, respectively. Consequently, there is an improved nutritional value of oil palm empty fruit bunches.
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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 | 0.000 | 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".