An Overview of Wild Edible Fungi Resource Conservation and Its Utilization in Yunnan
Bibliographic record
Abstract
As one of the province with the most abundant bio-diversity in China, Yunnan is also one of the most-concerned region in the world which is famous for its bio-diversity. The wild edible fungi resources in Yunnan are extremely rich and diverse, which account for nearly 40.7% of the world’s known resources and 90% of China’s known resource respectively. Serving as one important type of forest understory resource and product, the wild edible fungi has a large market due to its unique flavor, texture or special health care function. Therefore, sustainable utilization of the wild edible fungi resource is of paramount importance to develop the rural livelihood and furthermore conserve forest and animal resources in Yunnan. Integrating a large number of previous researches, this paper has summarized the current situations of edible fungi resources as well as the existing problems on production, food, medicine utilization, processing, trade and resource conservation inYunnan. Data showed that the wild edible fungi resources are playing extremely important roles on the peasant household incomes in the vast mountainous areas in Yunnan. For example, the fungi collection incomes can account for 50% to 80% of the household income in Xishuangbanna. However, the wild edible fungi resources are facing the problems of the reductions in resource types and quantities, unclear ownership, improper collection and conservation measures, unscientific management as well as other outstanding issues. The author point out that it is quite essential to clear the ownership, implement the community-based conservation strategies as well as to strengthen the researches on the scientific collection, conservation and breeding technologies in order to solve the problems in the effective conservation and sustainable utilization of the wild edible fungi resources in the future.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".