Implementing the Web-based 3D Coast Flood Disaster Simulation System
Bibliographic record
Abstract
The damage scale and damage area in the coast have been increased dramatically because of calamities such as typhoon, tidal wave, flood and storm. Especially, 409 cases, which reach to about 40.9 % of natural disasters of 1,000 cases for the recent 15 years have happened on coast area. More than 40 % of natural disasters also occurred every year is happening in coastland. Therefore, there is a great need to construct all related GIS database such as atmospheric phenomena (typhoon, tidal wave, flood and storm), harbor facility, harbor traffic and ebb and flow. Furthermore, the certain system should be developed and integrated with NDMS (National Disaster Management System) by using 3D web GIS technology. In this study, the coast disaster area management system was designed and developed by using 3D web GIS technique so that the coast disaster area could be monitored and managed in real time and in visual. Finally, the future disaster in coast area could be predicted scientifically. The coast has been very weak to calamity such as typhoon, tidal wave, flood and storm. Especially, 409 cases, which reach to about 40.9 % of natural disasters of 1,000 cases for the
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.020 | 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 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".