A Theoretical Framework for Integrating Edge-Enabled Generative AI and AR/VR in Disaster Management Decision Support
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The increasing complexity of climate-induced disasters demands innovative approaches to decision-making under uncertainty. Traditional management systems often fail to bridge the cognitive gap between immediate actions and long-term strategic planning, particularly in resource-constrained environments. This paper presents a theoretical framework integrating edge computing, generative AI, and AR/VR technologies to address cognitive barriers in sustainable disaster management decision-making. Drawing from cognitive load theory and using the 2012 Pakistan floods as a motivating case study, we propose a three-tier architecture combining probabilistic scenario generation with immersive visualization. The framework addresses three fundamental challenges: temporal distance in strategic planning, cognitive limitations in processing complex data, and the integration of real-time adaptability with long-term sustainability goals. Our key contribution lies in formalizing the relationship between immersive technologies and cognitive barrier reduction in disaster management through theoretical modeling. This conceptual framework is intended to guide future implementation efforts and empirical studies in disaster management systems.
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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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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