Keberlanjutan Usahatani Padi Sawah di Wilayah Daerah Aliran Sungai (DAS) Paguyaman Kabupaten Boalemo
Bibliographic record
Abstract
Wetland rice cultivation becomes crucial in sustaining the food supply amidst various challenges related to land use conversion. Given this issue, it is essential to assess the suitability and capacity of the land so that land production factors can become more efficient. This is also related to the establishment of sustainable agriculture. This research aims to analyze the sustainability of wetland rice farming in the Paguyaman River Basin region of Boalemo Regency, considering ecological, economic, institutional, and technological aspects. The study was conducted using a quantitative approach with a descriptive method among farmers in the Paguyaman and Wonosari Districts of Boalemo Regency. The data for this research consisted of primary data collected through questionnaires, interviews, and field observations. The questionnaire used in this research employed the Multidimensional Scaling (MDS) model. The subjects of this study included 30 respondents. The data analysis employed the Localization Index (LI), Specialization Index (SI), and sustainability analysis using the Multiaspect Sustainability Analysis (MSA) program. The research results indicate that the Localization Index (LI) is 0.3877, categorized as spread out, with a Specialization Index (SI) of 1.1094, categorized as specialized. Furthermore, the sustainability of wetland rice as a commodity is categorized as "Very Sustainable," with an average score of 79.64%. The results for each indicator show that the ecological aspect is rated as relatively good, while the economic, institutional, and technological aspects are rated as fairly good.
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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 | 0.000 |
| 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.017 | 0.002 |
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".