Smart Disaster Detection and Response System for Smart Cities
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, natural and human-induced disasters result in infrastructural damages, monetary costs, distresses, injuries and deaths. Unfortunately, climate change is strengthening the destructive power of natural disasters. In this context, Internet-of-Things (IoT)-based disaster detection and response systems have been proposed to cope with disasters and emergencies by improving the disaster detection and search and rescue missions during disaster response. Accordingly, IoT devices are used to collect data and help to identify hazards after disasters and to localize injured people. However, a solely IoT-based detection and response system will not be totally suitable for emergency response in smart cities, as the lack of connectivity with IoT devices might occur, due to breakages in communication infrastructures or network congestions. Therefore, we propose a novel architecture for smart disaster detection and response system for smart cities. We discuss the main building blocks of our envisioned smart system, as well as the critical challenges that will be faced ahead to implement our smart system.
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 | 0.001 | 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 | 0.001 | 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