Effects of Cultivation Substrates on Yield and Quality of <i>Ganoderma lucidum</i>
Bibliographic record
Abstract
Ganoderma lucidum, renowned for its rich polysaccharides and triterpenoid active compounds, possesses significant medicinal value, driving strong market demand.This study examines the effects of different cultivation substrates on the yield and quality of G. lucidum, aiming to identify substrates that can effectively enhance its yield and active compound content.The study evaluated various substrates, including sawdust, agricultural waste, and modified media, finding that specific lignocellulosic substrates, such as coconut sawdust, significantly increased the yield and biological efficiency of G. lucidum.Additionally, the incorporation of supplements like olive oil and copper was found to enhance the triterpenoid and phenolic compound content in G. lucidum.The findings indicate that optimizing substrate formulations and additives can improve the medicinal value and economic viability of G. lucidum production, providing a scientific basis for achieving efficient and sustainable cultivation.This study has significant practical implications for the development of the G. lucidum cultivation industry, suggesting future directions for further optimization of substrates and cultivation conditions to meet the market demand for high-quality G. lucidum.
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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".