MODEL KESESUAIAN LAHAN BERBASIS KERAWANAN BENCANA ALAM, UJI COBA: KOTA SEMARANG
Bibliographic record
Abstract
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 suitable in representing 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 91,25%. However, to be widely generazed, the model needs to be tested more, by applying in other locations having criteria regardingthe needs of the test.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.029 | 0.005 |
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".