Bibliographic record
Abstract
Kabupaten Banggai Kepulauan terletak di Provinsi Sulawesi Tengah yang merupakan salah satu provinsi prioritas nasional untuk bencana gelombang pasang di Indonesia berdasarkan BNPB. Banggai Kepulauan adalah kabupaten dengan bentuk kepulauan sehingga berpotensi terkena bahaya gelombang pasang terutama gelombang pasang yang berasal dari Samudera Pasifik. Parameter yang digunakan dalam menentukan pola bahaya gelombang pasang adalah arah angin, kelandaian pantai dan riwayat gelombang pasang. Berdasarkan hasil analisis, maka diketahui bahwa pola bahaya gelombang pasang di Kabupaten Banggai Kepulauan terdiri dari dua pola utama yaitu bahaya gelombang pasang dari Timur Laut dan Tenggara. Gelombang pasang dari Timur Laut berasal dari Samudera Pasifik, sedangkan gelombang pasang dari Tenggara berasal dari Laut Banda. Pada periode gelombang pasang Timur Laut yaitu pada bulan Februari, Maret, dan Desember, bahaya gelombang pasang tinggi terjadi di pesisir Utara, Timur, hingga Timur Laut Pulau Peling dengan ketinggian 1 - 3 meter. Pada periode gelombang pasang Tenggara, khususnya pada bulan Mei, Juni, dan Juli, tinggi gelombang pasang yang memasuki wilayah Perairan Selatan, Barat dan Tenggara Pulau Peling serta Teluk Tolo berkisar antara 1 hingga mencapai 3 meter.
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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 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".