Pertimbangan Perencanaan Tata Ruang Wilayah Berbasis Yurisdiksi di Kabupaten Kapuas Hulu
Bibliographic record
Abstract
Kapuas Hulu District is the upstream region of West Kalimantan Province, one of the districts with most of the area being a protected area, or 76% being a forest area (National Park, Protected Forest, and Production Forest). Obviously, the approach to spatial planning in Kapuas Hulu District is different from other regions that have smaller protected areas. Sustainable production is the basis for spatial utilization in Kapuas Hulu District. A jurisdictional approach to sustainable production areas is a suitable concept, as it holistically considers the economic, ecological, and social context within a landscape. Sustainable production areas are designed to simultaneously conserve important ecosystems, establish sustainable agricultural production, and improve the living conditions of local residents. The objective of this research is strategic recommendations and implementation of increased consideration of sustainability aspects in the spatial plan review process. The method used in this study involves a mixed approach that combines qualitative and quantitative methods to obtain comprehensive results. The analysis used is the Interpretative Structural Modeling (ISM) technique. The results of this study are strategic directions for spatial utilization based on the principles, criteria, and indicators of spatial planning, making it easier to carry out monitoring and evaluation and to ensure that the principles of sustainability are implemented.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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".