Bibliographic record
Abstract
Kajian Kelayakan Pembangunan Parkir Vertikal Kota Malang merupakan kajian yangditujukan untuk mengetahui tingkat kelayakan lokasi pembangunan bangunan parkir vertikal di Kota Malang. Analisa yang digunakan adalah analisa kebijakan, analisa kinerja parkir, analisa proyeksi kebutuhan parkir dan analisa kelayakan. Suevey Kinerja parkir dilaksanakan di empat titik lokasi. Jalan Gajahmada (ruas jalan Pemerintah Kota Malang), Jalan Gajahmada (depan Pemerintah Kota Malang), Jalan Tumapel, dan Jalan Mojopahit. Survey dilaksanakan weekday, weekend sabtu dan weekend minggu. Dieroleh indeks parkir tertinggi di Jalan Tumapel pada saat weekend minggu siang sebesar 0.30, sedagkan indeks parkir terendah terjadi di Jalan Gajahmada (depan Pemerintah Kota Malang) sebesar 0. Berdasarkan royeksi kebutuhan ruang parkir disimpulkan bahwa pada tahun 2024 jumlah total ruang parkir serta luas yang dibutuhkan 1.553 SRP dengan luas 17.860 m2. Analisis kelayakan bangunan parkir vertikal ditinjau dari empat aspek, yaitu aspek teknis, aspek lingkungan dan kesehatan, aspek ekonomi, serta aspek sosial..berdasarkan seluruh aspek anlisa kelayakan bahwa nilai lokasi eks DLH adalah 51 dan untuk lokasi Balaikota juga 51. Sehingga keduanya dikatakan layak untuk dibangun parkir vertikal.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.022 | 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".