Ethnobotany and traditional medicine of the inhabitants of the Pantanal Negro sub-region and the raizeiros of Miranda and Aquidauna, Mato Grosso do Sul, Brazil
Bibliographic record
Abstract
A survey on the use of medical plants was carried out in the rural communities of the Rio Negro sub-region of the Pantanal and the raizeiros from Aquidauana and Miranda municipalities, Mato Grosso do Sul, Brazil, in order to recover the ethnobotanical and ethnopharmacological knowledge of these communities. Structured questionnaires were run with the residents of eight farms and 12 raizeiros. The results reveal 25 botanical families, 45 genera and 48 species of medicinal plants used, six of which are indicated for kidney disturbances, six for urinary disturbances, five for inflammation treatment, 13 for stomach aches, 10 for respiratory disturbances, four for treating sprains, four for healing wounds, four as anti-diarrheaic and one as antipyretic, among other illnesses. The main family was Asteraceae, with 12 species used. The principal preparation methods of the medicinal herbs in the Rio Negro sub-region and surrounding areas were infusion (35) and, mostly, mixed with "chimarrão" or "mate quente", traditional beverage. Nineteen exotic species are used by the raizeiros, (39.58%), which indicates a strong influence of the urban environment. The traditional pantaneiros have greater knowledge of medicinal plants than the raizeiros, and they cited only five exotic species (16.1%).
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".