OPTIMASI PEMANFAATAN JALUR PEJALAN KAKI DI KAWASAN NIAGA TERPADU SUDIRMAN
Bibliographic record
Abstract
Adanya integrasi antara fasilitas pejalan kaki dengan moda transportasi massal menjadi salahsatu daya tarik kawasan niaga terpadu Sudirman. Dengan fasilitas penunjang yang memadai,jalur pejalan kaki belum dapat mengakomodasi kegiatan dan kebutuhan pejalan kaki secaraoptimal. Penelitian ini bertujuan untuk mengetahui apakah pemanfaatan jalur pejalan kakiKawasan SCBD sudah optimal sehingga dapat memfasilitasi pejalan kaki besertakegiatannya. Pendekatan penelitian campuran dengan melakukan observasi lapangan dalammenilai tingkat keoptimalan fasilitas pejalan kaki, kemudian menyimpulkan data berdasarkanstandar dan tingkat pengaruh masing–masing aspek terhadap pejalan kaki dan menyajikandata secara kuantitatif. Temuan dari penelitian ini bahwa fasilitas pejalan kaki di KawasanSCBD belum optimal karena minimnya ruang atau fasilitas sosial dan kesenjangan jumlahpejalan kaki pada jam sibuk dan di luar jam sibuk. Hasil penelitian menyimpulkan bahwapemanfaatan jalur pejalan kaki dapat dioptimalkan melalui program penyediaan fasilitas danpelebaran jalur.Kata kunci : Jalur Pejalan Kaki, Kawasan Niaga Terpadu, Aktivitas Pejalan Kaki
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.022 | 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".