SISTEM INFORMASI DINAS KELAUTAN DAN PERIKANAN KABUPATEN DOMPU PROVINSI NUSA TENGGARA BARAT BERBASIS WEBSITE MENGGUNAKAN FRAMEWORK BOOTSTRAP DAN CODEIGNITER
Bibliographic record
Abstract
Dinas Kelautan dan Perikanan Kabupaten Dompu Provinsi Nusa Tenggara Barat adalah lembaga pemerintah yang bertanggung jawab atas pengelolaan, pengembangan, dan perlindungan sumber daya kelautan dan perikanan. Salah satu peran pentingnya yaitu mengkoordinasikan kegiatan pengelolaan sumber daya kelautan dan perikanan, termasuk penangkapan ikan yang berkelanjutan, pengembangan budidaya perikanan serta pelestarian lingkungan laut. Saai ini Kelautan dan Perikanan sudah memiliki website company profile untuk menyajikan informasi terkait kantor tersebut, akan tetapi website tersebut memiliki fitur-fitur yang kurang lengkap seperti tidak adanya foto-foto dari unit kerja dari seluruh pegawai yang bekerja serta tidak adanya sistem validasi oleh Kapala Bidang pada saat admin menginputkan berita. Dengan dibuatnya sistem informasi ini, dapat memberikan aksesibilitas informasi kepada masyarakat terkait dengan layanan, program, kebijakan, dan kegiatan yang diselenggarakan oleh Dinas Kelautan dan Perikanan Kabupaten Dompu Provinsi Nusa Tenggara Barat.
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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.029 | 0.030 |
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".