Exploring the Species Diversity of Edible Mushrooms in Yunnan, Southwestern China, by DNA Barcoding
Bibliographic record
Abstract
Yunnan Province, China, is famous for its abundant wild edible mushroom diversity and a rich source of the world's wild mushroom trade markets. However, much remains unknown about the diversity of edible mushrooms, including the number of wild edible mushroom species and their distributions. In this study, we collected and analyzed 3585 mushroom samples from wild mushroom markets in 35 counties across Yunnan Province from 2010 to 2019. Among these samples, we successfully obtained the DNA barcode sequences from 2198 samples. Sequence comparisons revealed that these 2198 samples likely belonged to 159 known species in 56 different genera, 31 families, 11 orders, 2 classes, and 2 phyla. Significantly, 51.13% of these samples had sequence similarities to known species at lower than 97%, likely representing new taxa. Further phylogenetic analyses on several common mushroom groups including 1536 internal transcribed spacer (ITS) sequences suggested the existence of 20 new (cryptic) species in these groups. The extensive new and cryptic species diversity in wild mushroom markets in Yunnan calls for greater attention for the conservation and utilization of these resources. Our results on both the distinct barcode sequences and the distributions of these sequences should facilitate new mushroom species discovery and forensic authentication of high-valued mushrooms and contribute to the scientific inventory for the management of wild mushroom markets.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 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 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".