Pembuatan Peta 3D Urban Model Untuk Visualisasi Dampak Banjir
Bibliographic record
Abstract
3D modeling is a process to create 3D objects that you want to put in a visual form. A 3D model is a mathematical representation of any three-dimensional object (either inanimate or living). A model is technically graphical until it is visually displayed. Because 3D models are not limited to virtual space. A model can be displayed visually as a two-dimensional image through a process called 3D rendering, or used in non-graphical computer simulations and calculations. In this case, the geographic information system can present a form of modeling of a hydrological phenomenon such as flooding in an area. This study aims to analyze the flood and visualize it in the form of three-dimensional modeling to see the impact of a flood threat due to the Jelateng river’s overflow. This study emphasizes information related to the impact caused by the overflow of the Jelateng river. Making a 3D urban map model will be used as a representation of the appearance of the Jelateng river area and then it will be visualized using DEMNAS data on the arcscene with the animation manager so that the visualization can be seen according to the scenario that will be carried out. The results of the research will be published in a journal so that it can be a reference for some users who want to know related information from the research results
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.023 | 0.006 |
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".