Strategi Pemenuhan Ruang Terbuka Hijau (RTH) di Kecamatan Panakkukang Kota Makassar
Bibliographic record
Abstract
Abstrak. Tujuan dari penelitian ini adalah untuk menghitung luas ketersediaan dan kebutuhan Ruang Terbuka Hijau (RTH) di Kecamatan Panakkukang Kota Makassar, serta merumuskan strategi pemenuhan Ruang Terbuka Hijau di Kecamatan Panakkukang Kota Makassar. Penelitian ini adalah penelitian kualitatif yang diinterpretasikan secara deskriptif. Data yang diperoleh selanjutnya dianalisis menggunakan analisis kualitatif dengan metode Indeks Hijau-Biru Indonesia (IHBI) dan analisis deskriptif. (1) Hasil analisis yang diperoleh dari menghitung luas kebutuhan ruang terbuka hijau berdasarkan luas wilayah dan jumlah penduduk di Kecamatan Panakkukang sudah terpenuhi mencapai 30%, dengan melihat hasil perhitungan luas ketersediaan ruang terbuka hijau menggunakan metode Indeks Hijau-Biru Indonesia yaitu seluas 668,10 Ha dengan persentase 39,2% dari luas wilayah Kecamatan Panakkukang. (2) Strategi pemenuhan ruang terbuka hijau di Kecamatan Panakkukang yaitu mempertahankan dan menjaga kelestarian ruang terbuka hijau yang ada saat ini dengan memenuhi kriteria fungsi Ruang Terbuka Hijau yang terdiri dari fungsi ekologis, resapan air, ekonomi, sosial budaya, dan penanggulangan bencana.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.023 | 0.004 |
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".