Medicinal Plants Used by the Inhabitants of Alfred Nzo District Municipality in the Eastern Cape Province, South Africa
Bibliographic record
Abstract
Plant species used as herbal medicines play an important in the provision of primary healthcare in several rural communities. The current study was aimed at documenting medicinal plants used by the inhabitants of Alfred Nzo District Municipality in the Eastern Cape province, South Africa. Information on medicinal plants used for primary healthcare was collected through open-ended interviews with a sample of 124 participants selected via snowball-sampling technique between April 2017 and May 2018. A total of 34 plant species and one fungus species representing 20 families were used in the treatment of 13 different human diseases. The major diseases treated by the documented species included respiratory system, pain, sores and wounds, infections and infestations, digestive system, blood and cardiovascular system, fever and malaria, general ailments, reproductive system and sexual health and mental disorders. Popular herbal medicines with relative frequency citation (RFC) values exceeding 0.50 included Bulbine frutescens, Clivia miniata var. miniata, Elephantorrhiza elephantina, Centella asiatica, Hypoxis hemerocallidea, Dicerothamnus rhinocerotis, Leonotis leonurus, Agapanthus africanus and Datura stramonium. Such repository of medicinal plants and fungi reinforces the need for an evaluation of their biological activities as a basis for developing future medicines and pharmaceutical products.
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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 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".