Wild edible mushroom knowledge and use in five forest communities in central México
Bibliographic record
Abstract
Wild edible mushrooms are non-timber forest products highly valued as food supplements and a source of income for rural communities. The objective is to quantify the use and knowledge of wild edible mushrooms across forest socio-ecosystems of central México. We conducted 40 household structured surveys in five Mestizo communities in the state of Michoacán (central-western México) to evaluate their mycological knowledge. We also compare the knowledge of these Mestizo people with that of the surrounding Indigenous communities. We compiled and updated a list of the wild edible mushrooms used in the whole state, which contains 243 mushroom species used out of the 371 used in México. Here, in these five communities, we recorded 13 species currently used (a median of seven). In four communities, 1 kg of mushrooms on average is collected per harvesting trip, whereas in one of the communities, people extracted 3 kg of mushrooms per trip on average and 5–15 kg per season, respectively. The most used and valued species were Amanita basii, Amanita jacksonii, and Hypomyces lactifluorum. Despite being highly valued resources, land managers do not include mushrooms in the decision-making process for planning forest management. We found that knowledge and use of wild edible mushrooms in Mestizo communities are lower than those in regional Indigenous communities in localities with similar climate and forest vegetation. Fungal resources like wild edible mushrooms in the area are therefore underutilized, making forested areas more vulnerable to land-use change. Promotion of mycological knowledge may contribute to enhancing forest conservation policies.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".