Analisis Karakteristik dan Potensi Lahan Pekarangan untuk Mendukung Penganekaragaman Konsumsi Pangan Keluarga di Kecamatan Kepil Kabupaten Wonosobo
Bibliographic record
Abstract
The potential of the yard contributes to food availability and diversification of food consumption. Utilization for agricultural activities will provide benefits in the form of the availability of healthy and nutritious fresh food. This study aims to analyze the characteristics and potential of yard land based on supporting the diversification of family food consumption in Kepil District. This study was conducted in Kepil District from March to June 2025. The study was conducted using a survey method, sampling with stratified random sampling. The results showed that the majority of yard land was in the narrow category with a dominance of the front zone for cultivating vegetables and spices. A total of 10 types of plants that are often found in more than 50% of yards in Kepil District are; red cayenne pepper, curly chili, spring onions, eggplant, tomatoes, celery, caisim, ginger, turmeric and lemongrass. Food diversification in Kepil District found 5 groups of fresh food from plants 5 types of cereal group food, 26 types of vegetable group food, 14 types of spice group food, 22 types of fruit group food, 1 type of freshener and sweetener group food, 1 type of poultry group food, 3 types of mammalian animal product group food, 3 types of fish group food. The pattern of utilization of food sources in the yard of Kepil District is as a source of carbohydrates 5.33%, fat 4.00%, minerals 49.33%, protein 9.33% and vitamins 32.00%.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".