PENATAAN POTENSI EKOWISATA MANGROVE PANTAI LARITI UNTUK MENINGKATKAN SEKTOR EKONOMI MASYARAKAT DESA SORO KEC. LAMBU KAB. BIMA.
Bibliographic record
Abstract
Perkembangan Ekowisata mangrove akhir-akhir ini mulai mendapatkan sambutan yang positif dari masyarakat Bima, karena Ekowisata mangrove mengedepankan pelestarian lingkungan dan menjaga ekosistem laut dan daratan, serta dapat meningkatkan sektor ekonomi masyarakat khususnya masayarakat yang berhubungan langsung dengan wilayah pantai mangrove.Keberadaan ekowisata mangrove dan wisata Pantai Lariti juga memberikan dampak positif dan negatif bagi masyarakat Desa Soro, Tujuan penelitian ini adalah Untuk mengembangkan sumber potensi ekowisata khususnya ekowisata mangrove melalui perencanaan pengembangan ekowisata yang tersebar di Kabupaten Bima, Menambah pengetahuan tentang penataan lokasi ekowisata mangrove, Menyediakan informasi yang jelas bagi masyarakat yang akan berkunjung kelokasi ekowisata mangrove, serta sebagai masukkan untuk mengelola ekowisata mangrove.Setelah penelitian dilakukan diharapkan kesanggupan mengembangkan kegiatan liburan yang berwawasan lingkungan, pelestarian alam terutama dalam melestarikan hutan bakau sebagai pelindung garis pantai agar tidak terjadi abrasi serta sebagai konservasi bagi flora dan fauna yang ada di lingkungan pantai.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.010 | 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".