Disaster Response and Mitigation Support Technology for All-Hazards in Tokyo Metropolitan Area
Bibliographic record
Abstract
In Theme 7-2 of SIP Disaster Prevention (Enhancement of Social Resiliency against Natural Disaster of Cross-ministerial Strategic Innovation Promotion Program), we implemented the two subthemes to develop the disaster response and mitigation technology effective for the complex disaster caused by earthquake and flood by torrential rain in megalopolis such as Tokyo metropolitan area; “Subtheme 1: Development of Application Software for Supporting All-Hazards Management in Megalopolis and Commercial Areas around Large Terminal Stations,” and “Subtheme 2: Sustainable Development of Local Disaster Prevention Technology with Visualization Application.” In the former, we formulated behavioral guidelines of central city areas during disasters based on the hazard/risk assessment, and developed an application software for PC/smartphone to support emergency management by delivering relevant information to civilians and disaster response workers during the disaster. Especially, the application would reduce secondary disasters, such as the confusion/panic by the huge number of crowds. In the latter, to “efficiently utilize the limited time, human resources and goods and to minimize damage” at the time of the disaster, we developed a “travel support application,” which can efficaciously “assign” workers to various tasks (the events that require a response) that are spatially distributed at the occurrence of disaster, “navigate” by identifying optimal routes for patrol and “monitor” progress.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".