MODEL KESESUAIAN LAHAN BERBASIS KERAWANAN BENCANA ALAM, UJI COBA: KOTA SEMARANG
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Bibliographic record
Abstract
<span style="font-size: 11.0pt; mso-bidi-font-size: 10.0pt; font-family: 'Amasis MT','serif'; mso-fareast-font-family: Calibri; mso-fareast-theme-font: minor-latin; mso-bidi-font-family: 'Times New Roman'; mso-bidi-theme-font: minor-bidi; mso-ansi-language: EN-AU; mso-fareast-language: IN; mso-bidi-language: AR-SA;" lang="EN-AU">Indonesian regions are prone to natural disasters. For this, Law 26/2007 on Spatial Planning orders that disaster mitigation is an important. This paper aims at developing a spatial model for suitability analysis, mainly considering physical and disaster prone conditions. The model is a raster based-GIS weighted scoring model. The model is applied in Semarang City with the consideration has various topographical conditions, from flat in the North and hilly in the South.The application shows that the model is </span><span style="font-size: 11.0pt; mso-bidi-font-size: 10.0pt; font-family: 'Amasis MT','serif'; mso-fareast-font-family: Calibri; mso-fareast-theme-font: minor-latin; mso-bidi-font-family: 'Times New Roman'; mso-bidi-theme-font: minor-bidi; mso-ansi-language: IN; mso-fareast-language: IN; mso-bidi-language: AR-SA;" lang="IN">suitable in</span><span style="font-size: 11.0pt; mso-bidi-font-size: 10.0pt; font-family: 'Amasis MT','serif'; mso-fareast-font-family: Calibri; mso-fareast-theme-font: minor-latin; mso-bidi-font-family: 'Times New Roman'; mso-bidi-theme-font: minor-bidi; mso-ansi-language: EN-AU; mso-fareast-language: IN; mso-bidi-language: AR-SA;" lang="EN-AU"> represent</span><span style="font-size: 11.0pt; mso-bidi-font-size: 10.0pt; font-family: 'Amasis MT','serif'; mso-fareast-font-family: Calibri; mso-fareast-theme-font: minor-latin; mso-bidi-font-family: 'Times New Roman'; mso-bidi-theme-font: minor-bidi; mso-ansi-language: IN; mso-fareast-language: IN; mso-bidi-language: AR-SA;" lang="IN">ing</span><span style="font-size: 11.0pt; mso-bidi-font-size: 10.0pt; font-family: 'Amasis MT','serif'; mso-fareast-font-family: Calibri; mso-fareast-theme-font: minor-latin; mso-bidi-font-family: 'Times New Roman'; mso-bidi-theme-font: minor-bidi; mso-ansi-language: EN-AU; mso-fareast-language: IN; mso-bidi-language: AR-SA;" lang="EN-AU"> land suitability in three categories, i.e. low, medium, and high flexibility of development. The validation, done by comparing the model output and reality, shows that its accuracy is 91</span><span style="font-size: 11.0pt; mso-bidi-font-size: 10.0pt; font-family: 'Amasis MT','serif'; mso-fareast-font-family: Calibri; mso-fareast-theme-font: minor-latin; mso-bidi-font-family: 'Times New Roman'; mso-bidi-theme-font: minor-bidi; mso-ansi-language: IN; mso-fareast-language: IN; mso-bidi-language: AR-SA;" lang="IN">,</span><span style="font-size: 11.0pt; mso-bidi-font-size: 10.0pt; font-family: 'Amasis MT','serif'; mso-fareast-font-family: Calibri; mso-fareast-theme-font: minor-latin; mso-bidi-font-family: 'Times New Roman'; mso-bidi-theme-font: minor-bidi; mso-ansi-language: EN-AU; mso-fareast-language: IN; mso-bidi-language: AR-SA;" lang="EN-AU">25%. However, to be widely generazed, the model need</span><span style="font-size: 11.0pt; mso-bidi-font-size: 10.0pt; font-family: 'Amasis MT','serif'; mso-fareast-font-family: Calibri; mso-fareast-theme-font: minor-latin; mso-bidi-font-family: 'Times New Roman'; mso-bidi-theme-font: minor-bidi; mso-ansi-language: IN; mso-fareast-language: IN; mso-bidi-language: AR-SA;" lang="IN">s</span><span style="font-size: 11.0pt; mso-bidi-font-size: 10.0pt; font-family: 'Amasis MT','serif'; mso-fareast-font-family: Calibri; mso-fareast-theme-font: minor-latin; mso-bidi-font-family: 'Times New Roman'; mso-bidi-theme-font: minor-bidi; mso-ansi-language: EN-AU; mso-fareast-language: IN; mso-bidi-language: AR-SA;" lang="EN-AU"> to be tested</span><span style="font-size: 11.0pt; mso-bidi-font-size: 10.0pt; font-family: 'Amasis MT','serif'; mso-fareast-font-family: Calibri; mso-fareast-theme-font: minor-latin; mso-bidi-font-family: 'Times New Roman'; mso-bidi-theme-font: minor-bidi; mso-ansi-language: IN; mso-fareast-language: IN; mso-bidi-language: AR-SA;" lang="IN"> more</span><span style="font-size: 11.0pt; mso-bidi-font-size: 10.0pt; font-family: 'Amasis MT','serif'; mso-fareast-font-family: Calibri; mso-fareast-theme-font: minor-latin; mso-bidi-font-family: 'Times New Roman'; mso-bidi-theme-font: minor-bidi; mso-ansi-language: EN-AU; mso-fareast-language: IN; mso-bidi-language: AR-SA;" lang="EN-AU">, by applying in other locations having criteria </span><span style="font-size: 11.0pt; mso-bidi-font-size: 10.0pt; font-family: 'Amasis MT','serif'; mso-fareast-font-family: Calibri; mso-fareast-theme-font: minor-latin; mso-bidi-font-family: 'Times New Roman'; mso-bidi-theme-font: minor-bidi; mso-ansi-language: IN; mso-fareast-language: IN; mso-bidi-language: AR-SA;" lang="IN">regarding</span><span style="font-size: 11.0pt; mso-bidi-font-size: 10.0pt; font-family: 'Amasis MT','serif'; mso-fareast-font-family: Calibri; mso-fareast-theme-font: minor-latin; mso-bidi-font-family: 'Times New Roman'; mso-bidi-theme-font: minor-bidi; mso-ansi-language: EN-AU; mso-fareast-language: IN; mso-bidi-language: AR-SA;" lang="EN-AU">the needs of the test.</span>
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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 it