Informasi Kesehatan melalui Leaflet dan Penyuluhan di Desa Kemingking Luar Kec. Taman Rajo
Bibliographic record
Abstract
Kemingking Luar Village Taman Rajo District Muaro Jambi Regency is a village that has an area of 820,0000 hectares (hectares) and has a population of ± 483 people. The border of the village of Kemingking Luar is bordered by the village of Muaro Jambi. Problems in the village of Kemingking Luar namely rheumatism and hypertension, based on field surveys for hypertension and rheumatism in the Village of Kemingking Luar are still very bad because of the work of the majority of farmers. In addition, the survey results also show that family medicinal plants (TOGA) in Kemingking Luar Village have almost 70% already existed, only the use of TOGA is still lacking because people do not know the benefits and processing of TOGA. for self-medication and to be used as a product of economic value for the people of Kemingking Luar Village, and other problems that occur in Kemingking Luar Village, such as smoking habits, and the lack of community activities to lead a healthy life. Based on this data, students from the Real Work Lecture at Kemingking Luar Village provide counseling and skills to the community of Kemingking Luar Village such as Rheumatism, Gout, Blood Sugar, DAGUSIBU, and Blood Pressure counseling, correct treatment methods, and how to manage TOGA into a TOGA form. valuable and beneficial products for the surrounding community, and utilize TOGA.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.091 | 0.021 |
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".