Inventory of Medicinal Plants for Male and Female Reproductive Health by Ethnic Javanese and Ethnic Dayak Seberuang in Balai Harapan Village, Tempunak District, Sintang Regency
Bibliographic record
Abstract
Introduction: Knowledge of the use of plants as medicine is one of the local wisdom of the Javanese and Dayak ethnic communities of Seberuang, Balai Harapan Village, which has been passed down from generation to generation by their ancestors. This research aims to inventory plants that have medicinal properties for male and female reproductive health which are used by the people of Balai Harapan Village, especially the Javanese and Dayak Seberuang ethnic groups. Methods: This research used a triangulation technique which is a combination of interviews, observation, and documentation of plant types for male and female reproductive health. The data obtained in this research analyzed and presented narratively for each plant and accompanied by pictures and tables. Results: There are 26 types of medicinal plants used by the Javanese and Dayak Seberuang ethnic groups to support reproductive health. Conclusion: The plants used as medicine for male and female reproductive health by the Javanese and Dayak Seberuang ethnic groups in Balai Harapan Village come from several families, namely Zingiberaceae, Asteraceae, Poaceae, Lamiaceae, Fabaceae, Polypodiaceae, Rubiaceae, Caricaceae, Melastomataceae, Gleicheniaceae, Dilleniaceae, Phylanthaceae, Blechnaceae, Schizaeaceae, Piperaceae, Moraceae, and Asparagaceae. The most common types of plants used come from the Zingiberaceae family.
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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 | 0.001 | 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 | 0.001 |
| 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".