Pengegmbangan Jalur Evakuasi Hutan Pinus Rahong, Kecamatan Pengalengan, Kabupaten Bandung
Bibliographic record
Abstract
Abstract. Development of evacuation routes in the Rahong pine forest tourist spot, Pengalengan sub-district, Bandung regency. The writing of this thesis has a direction in developing evacuation routes in the Rahong Pengalengan pine forest, the purpose of this study is to develop existing evacuation routes so that they are not just formalities. With the method used in the study, namely Network analysis. The data used are primary data obtained from surveys and interviews with the community and visitors and secondary data obtained from national and international journals. The results of this study are to show that the development of evacuation routes is made to facilitate evacuation in disaster situations and improve visitor safety and reduce the risk of disaster. Abstrak. Pengembangan jalur evakuasi pada tempat wisata hutan pinus rahong, kecamatan pengalengan, kabupaten bandung. Penulisan skripsi ini mempunyai arah dalam mengembangan jalur evakuasi pada hutan pinus rahong pengalengan, tujuan dari penelitian ini untuk mengembangan jalur evakuasi yang telah ada sehingga tidak hanya sebagai formalitas aja. Dengan metode yang digunakan dalam penelitian yaitu analisis Jaringan. Data yang digunakan berupa data primer yang diperoleh dari survey dan wawancara dengan masyarakat dan pengunjung serta sekunder yang diperoleh dari jurnal nasional maupun internasional. Hasil penelitian ini untuk menunjukan bahwa pengembangan jalur evakuasi dibuat agar mempermudah dalam melakukan evakuasi dalam situasi bencana dan meningkatkan keselamatan pengunjung dan mengurangi risiko dari bencana.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".