Manajemen Basisdata Dan Penyebarluasan Informasi Spatial Pemerintah Daerah Melalui Pembangunan Sulawesi Geographic Information System (Gis)
Bibliographic record
Abstract
Geographical Information System (GIS) berbasis web (webGIS) dipilih sebagai tools dalam pengelolaan basisdata dan penyebarluasan informasi mengenai Sulawesi oleh Canadian International Development Agency (CIDA). CIDA dalam strategi pembangunanya tahun 2004 – 2009 memfokuskan lokasi program pada Pulau Sulawesi. Untuk itu dibutuhkan sebuah sistem basisdata yang dapat mengintegrasikan beragam data, memvisualisasikan data dalam bentuk spasial, dan memungkinkan analisa data, serta dapat diakses dari berbagai lokasi di belahan bumi. Sistem basisdata dibangun dengan prinsip Relational Database Management System (RDMS) baik data spasial dan attribute dalam suatu aplikasi basisdata oracle 9i dan kombinasi aplikasi webserver dan mapserver dari geomedia webmap generasi terbaru. Aplikasi memuat variasi tema spasial yang luas terdiri dari tema lingkungan, keuangan pemerintah daerah, kemiskinan, donor, tujuan pembangunan milenium (Millenium Development Goal - MDG), Indeks pembangunan manusia (HDI) dan tataruang dengan tingkatan administrasi desa, kecamatan, kabupaten hingga provinsi. Dengan pembangunan aplikasi ini diharapkan ada proses pembelajaran dari sisi teknis bagi institusi lokal dan menjadi spatial portal bagi pemerintah daerah untuk mengelola basisdata dan penyebarluasan informasi.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.009 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.011 | 0.009 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.047 | 0.021 |
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".