Knowledge and use of wild edible plants in rural communities along Paraguay River, Pantanal, Brazil
Bibliographic record
Abstract
BACKGROUND: Wild plants are used as food for human populations where people still depend on natural resources to survive. This study aimed at identifying wild plants and edible uses known in four rural communities of the Pantanal-Brazil, estimating the use value and understanding how distance to the urban areas, gender, age and number of different environments available in the vicinity can influence the knowledge and use of these plants by local people. METHODS: Data on edible plants with known uses by communities were obtained through semi-structured interviews. A form with standardized information was used for all communities in order to obtain comparable data for analysis. For the quantitative analysis of the factors that could influence the number of species known by the population, a generalized linear model (GLM) was conducted using a negative binomial distribution as the data consisted of counts (number of citations). RESULTS: A total of 54 wild species were identified with food uses, included in 44 genera and 30 families of angiosperms. Besides food use, the species are also known as medicine, bait, construction, technology and other. The species with the highest use value was Acrocomia aculeata. Older people, aged more than 60 years, and those living in more remote communities farther from cities know more wild edible plants. Statistical analysis showed no difference regarding gender or number of vegetation types available in the vicinity and the number of plants known by locals. CONCLUSION: This study indicated more knowledge retained in communities more distant from the urban area, indifference in distribution of knowledge between genders and the higher cultural competence of elderly people in respect to knowledge of wild edible botanicals.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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 teacher head, 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".