Artificial Intelligence implementation for natural Disasters Management: systematization of approaches and risk clustering
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
Every year in one region or another of the world there are constant natural disasters (severe river floods, dam and levee breaks, earthquakes, storms and hurricanes, forest and peat fires). This research systematizes approaches to assessing the possibility of using artificial intelligence (AI) in natural disasters. The authors are the first to systematize the risks of using this technology for disaster management. Risk clustering was carried out based on the methodology proposed by the International Telecommunication Union to identify four stages within the disaster management cycle – mitigation, preparedness, response and recovery. The paper shows how the use of AI in disaster management allows, on the one hand, to increase the efficiency of specialists at all four identified stages. On the other hand, clustering the risks of using AI within this subject area from the point of view of management stages allows us to identify three main areas of further work related to data quality and optimal provision of ICT infrastructure.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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