AI-Driven Integrated Platform for Comprehensive Flood and Cyclone Disaster Management
Bibliographic record
Abstract
Disaster Management, a strategy or an activity that involves risk reduction planning and effective preparations for all stages of a crisis cycle, faces challenges in synchronization of the flow of information resulting in constant communication problems. This coordination imbalance leads to vulnerabilities in communities and makes them unprepared to respond and recover effectively. To fill these gaps, a new integrated inclusive, and accessible AI-driven platform, Surakshit Bharat has been introduced to mitigate the existing gap as mentioned above. It would integrate government agencies, NGOs, volunteers, and citizens, ensuring those who are most at risk can receive assistance and aid relief as fast as possible. Equipped with multiple technologies, the platform would be capable of sending out alerts in real time, remote assistance, a voice interface, and gamified educational training to improve self-efficacy and efficiency during preparation and rescue efforts. Elders and people with low literacy levels can easily access the platform through a simple navigated interface and multilingual guides. Utilizing cutting-edge technologies with a focus on inclusivity, the platform portrays itself as a comprehensive, integrated, and innovative solution in the crisis management 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.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.004 |
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".