Perencanaan Tata Ruang Terbuka Hijau Sesuai Peraturan Daerah Kota Denpasar Nomor 27 Tahun 2011
Bibliographic record
Abstract
The declining quality and quantity of green open space in urban areas has caused a decrease in the quality of the environment. Therefore, it is necessary to conduct a research on the Green Open Spatial Planning, especially in Denpasar City the legal provisions of which has been regulated in Regional Regulation No. 27 of 2011. This research analyzes the planning for the use of Green Open Spatial and the mechanism for changing the Green Open Spatial to change its function to become Spatial Settlement. The method used in this research was a normative research method, in which legal data collection was carried out by recording library studies, document studies, information and explanations obtained both from the Laws, Government Regulations and other Regulations that can be further examined which related to this problem. Data analysis in this research was carried out systematically by classification of legal materials to facilitate the analysis work, then Legal materials obtained are then subjected to discussion and grouping into certain sections. The results found that the Green Open Space is an area dominated by plants that are built for protection functions. The pattern of spatial use as a basis for the Denpasar City Government sets Green Open Spaces namely Settlements and Public Facilities. Changes in the pattern of utilization of green open spaces have changed the function resulting in the realization of optimal urban spatial planning. This happened because of the weak awareness of the people of Denpasar City.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 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) | 0.017 | 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".