Survey and Use of Medicinal Plants in an Urban District in the state of Piaua, Northeastern Brazil
Bibliographic record
Abstract
The use of plants for the treatment and cure of diseases is as old as the human species and is widely used by most of the world population as a therapeutic resource, mostly among rural populations, though less noticeable in the urban sphere. Existing data on the medicinal use of plants for the urban portion of society is still poor, and lack information that can trace a better ethnobotanical profile. Thus, this study aimed to collect data on the knowledge and use of medicinal plants, in the context in which the inhabitants of an urban district are inserted. Data collection was performed using a semi-structured form in 80 homes in the district Alto Santa Maria, in the urban area of the city of ParnaAba, northern Piaua. Twelve species were mentioned to be used by residents, such as boldo, eucalipto, malva, mastruz and erva-cidreira, especially the first one, which is the most used plant by residents in the district. In order to identify therapeutic indications of great importance, we used the Informant Consensus Factor (ICF). The predominant prescription use was indicated as conditions related to diseases of the genitourinary system and kidney stones, skin and nail diseases, dermatitis, and endocrine, metabolic and nutritional disorders, as indicated by ICF. With the development of this research we found that plants with medicinal potential represent a strong resource for the population studied in the treatment of several diseases, and a great degree of use was noticeable.
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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 | 0.001 |
| 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 | 0.000 |
| 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".