PENGEMBANGAN RUANG TERBUKA HIJAU DENGAN PENDEKATAN KOTA HIJAU DI KOTA KANDANGAN
Bibliographic record
Abstract
According to Act No 26/2007 about Spatial Planning, each region is required to provide 30% of its territory as green open space (RTH), sharing of 20% as public RTH and 10% as private RTH. Ministry of Public Works introduced the Green City Development Program (P2KH) <!--[if supportFields]> ADDIN EN.CITE Kementerian Pekerjaan Umum 2012 117 (Kementerian Pekerjaan Umum, 2012) 117 117 50 Kementerian Pekerjaan Umum, Program Pengembangan Kota Hijau (P2KH) Panduan Pelaksanaan 2012 Jakarta Kementerian Pekerjaan Umum <![endif]-->(Kementerian Pekerjaan Umum, 2012)<!--[if supportFields]><![endif]--> to assist the implementation of the mandate of this Act. This study aims to identify and predict the needs of public RTH in Kandangan City and organize referrals for expansion using Green City approach. The needs of RTH is calculated based on vast territory, population and comfort index. Comfort index of Kandangan City are in comfortable range. Based on vast territory, it takes 735,39 ha land for public RTH. Based on population, Kandangan City requires 170,81 ha. Atribute of Gren City approach used is Green Open Space. Compared with Zoning, Regulation, there should be more area needed for public RTH as green belt.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.123 | 0.032 |
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".